From 2c6637295087c208080edece25e4fe81bbe1efd5 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=EC=9D=B4=EA=B2=BD=EC=B0=ACB?= <nahcklee@hanatour.com>
Date: Mon, 1 Jan 2024 17:59:48 +0900
Subject: [PATCH] =?UTF-8?q?sasrec=20=EC=A0=84=EC=B2=98=EB=A6=AC=20?=
 =?UTF-8?q?=EC=A4=91?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .gitignore                    |    2 +
 SASRec/daum_train_dir/log.txt |   94 -
 SASRec/test.ipynb             | 5163 +--------------------------------
 3 files changed, 3 insertions(+), 5256 deletions(-)

diff --git a/.gitignore b/.gitignore
index 61a70ea..9722942 100644
--- a/.gitignore
+++ b/.gitignore
@@ -11,3 +11,5 @@ venv/
 */.ipynb_checkpoints
 
 .env
+
+*.pth
diff --git a/SASRec/daum_train_dir/log.txt b/SASRec/daum_train_dir/log.txt
index 930743e..e69de29 100644
--- a/SASRec/daum_train_dir/log.txt
+++ b/SASRec/daum_train_dir/log.txt
@@ -1,94 +0,0 @@
-(0.044373985099689615, 0.09300769488123119) (0.0436886654152135, 0.09611520428667113)
-(0.045958140814474274, 0.10003368137420007) (0.04205997193413812, 0.09044972208185953)
-(0.044356783572856405, 0.0970873786407767) (0.044191534035070534, 0.09572536722231391)
-(0.04536698385636212, 0.09559811827956989) (0.041851336699796224, 0.09435227082285906)
-(0.04472889149772005, 0.0956288728856138) (0.04328474266361943, 0.09551034134857911)
-(0.048239726844116405, 0.09866666666666667) (0.04731443834971107, 0.10084591142809753)
-(0.04444361392809496, 0.09430156328794756) (0.04332403218243672, 0.09416818106723938)
-(0.04498127493582461, 0.09726848767488341) (0.04525999418504957, 0.10093582887700535)
-(0.04632685236601284, 0.0988681757656458) (0.042796153361988165, 0.09431438127090301)
-(0.04468345576295705, 0.09794497845541929) (0.04491325008922685, 0.09681188449340677)
-(0.046945052656717834, 0.09722222222222222) (0.04322472167728918, 0.09549817941079113)
-(0.047108773451795455, 0.1019594818997011) (0.04729820852430967, 0.10666446171655367)
-(0.04439505611152801, 0.09386523633925578) (0.04408586029639218, 0.0959680106631123)
-(0.044713080324515236, 0.09448425262456257) (0.043471028547191266, 0.09427048634243837)
-(0.04542152430980709, 0.09690893901420217) (0.04214566700896336, 0.09306599832915623)
-(0.04609101754429701, 0.09804571050016561) (0.03956568400189815, 0.08898516913847693)
-(0.04333023959747806, 0.0940959409594096) (0.042723565048111825, 0.0953171703752906)
-(0.044794015836273744, 0.09756909756909757) (0.04618120097960042, 0.09858215179316097)
-(0.043662183688253316, 0.09410979476055398) (0.043360224251974784, 0.09529372496662217)
-(0.04498876556460492, 0.09571619812583668) (0.04272734669658938, 0.09306963603124481)
-(0.04202379984043614, 0.09075797872340426) (0.04311992508752632, 0.0947403910991234)
-(0.045268512141080894, 0.09768766756032171) (0.0468607209114692, 0.10271802567950641)
-(0.04359844583233627, 0.09207459207459208) (0.04612020241453335, 0.09836065573770492)
-(0.04608093283742416, 0.09939859672569329) (0.04607346748579972, 0.09966386554621849)
-(0.04526191445994982, 0.0961344537815126) (0.04325749151363178, 0.09676879290138958)
-(0.04321233420344863, 0.09391622340425532) (0.042122987863489766, 0.09336238685886691)
-(0.04515464679552393, 0.09449859573765075) (0.0440012218333394, 0.09720394736842106)
-(0.046830484903936265, 0.09821728893373696) (0.04373298824276087, 0.0968651517664621)
-(0.04497007068711003, 0.09635761589403974) (0.04416763488411565, 0.096169928081619)
-(0.04654062510085091, 0.09937888198757763) (0.0438247729395723, 0.09775694676933377)
-(0.04425386183798519, 0.09374478732276897) (0.045890680325843, 0.09981669721713048)
-(0.04753692353344556, 0.10040774719673802) (0.04764233375044716, 0.10197258441992645)
-(0.044033521210461134, 0.09535780124015418) (0.04722631472039476, 0.10424710424710425)
-(0.045047601234450986, 0.09595624792840571) (0.042727357430090054, 0.09519054751206524)
-(0.04560667788898872, 0.10005027652086476) (0.044965005745442904, 0.09879235155987924)
-(0.04317580382931966, 0.09181471027727046) (0.04356802299458313, 0.09647216184584517)
-(0.045552122060880905, 0.09863832613749585) (0.04359571461945084, 0.09581646423751687)
-(0.04433883683392936, 0.09640145262462858) (0.04604111340064984, 0.10028413839211098)
-(0.042727681195515996, 0.09155285786545271) (0.04187160103789263, 0.09353970390309556)
-(0.042262878514634006, 0.09264853977844914) (0.043341495840876695, 0.09636941609503095)
-(0.048414427820394935, 0.10634359750967524) (0.04350931952536367, 0.09576612903225806)
-(0.04671164198918663, 0.1009695753928452) (0.04202857359608118, 0.09141135107985937)
-(0.041498395380641026, 0.09093965372331485) (0.045108944375966256, 0.09820379385596777)
-(0.047318505487230225, 0.09931219594027847) (0.04436594803821674, 0.09668785547005687)
-(0.04695970548119968, 0.09842061512884455) (0.043601732380282754, 0.09605662285136501)
-(0.0449643100808555, 0.0961730449251248) (0.04102761974235967, 0.0897796918999503)
-(0.04410275170908447, 0.09142006943296413) (0.044811550756412646, 0.09560067681895093)
-(0.04609206346898147, 0.09764253469319512) (0.04118232470974501, 0.0934203917629332)
-(0.04421181471998444, 0.09468244707451241) (0.04208845172760542, 0.09323736190157349)
-(0.04822891734139953, 0.09991603694374475) (0.045479527331409, 0.0999172870140612)
-(0.044924124182479167, 0.09650443217929419) (0.04404108099165882, 0.09601600266711119)
-(0.04716903475909976, 0.09900823667843335) (0.04326355498307944, 0.0970729287249876)
-(0.04651980500422951, 0.09908409658617819) (0.045423030683618325, 0.10120768838237795)
-(0.045979672194898805, 0.09620253164556962) (0.0410301792563652, 0.09124704291990537)
-(0.04467948579279559, 0.09312823942484534) (0.04557738629934526, 0.09626653273062113)
-(0.04566795643220165, 0.0969961402919953) (0.0443693817307125, 0.09696359671196109)
-(0.04670630859570594, 0.09910366987992558) (0.044419329958988354, 0.09523020867837032)
-(0.04457919923969163, 0.09336650082918739) (0.04433661357939026, 0.09914443885254152)
-(0.04410684240367229, 0.0961250623648761) (0.04353721992146174, 0.0949748743718593)
-(0.04446339230131681, 0.09572335449381891) (0.0466181967120369, 0.0995995995995996)
-(0.04699288712446491, 0.09991617770326906) (0.04399123131071464, 0.09655172413793103)
-(0.044141877681033906, 0.09600935047587243) (0.04360320569806565, 0.09500506243671954)
-(0.04853040622486531, 0.10468227424749164) (0.04449082958047818, 0.09770788020746193)
-(0.04648920916138218, 0.09846717760746418) (0.04377790334516103, 0.09737454303755401)
-(0.04388210836652969, 0.09391711229946524) (0.04268150507603442, 0.0933422549347608)
-(0.041551298589270716, 0.08895653644512092) (0.045356184468011054, 0.09665489998319045)
-(0.04570014579074976, 0.09669771380186283) (0.047086365883282716, 0.10132974246759804)
-(0.045210775454608235, 0.09725727746929161) (0.04590632840884281, 0.0993322203672788)
-(0.0446139393887298, 0.09472803347280334) (0.042652613930647655, 0.09273182957393483)
-(0.045129543140635865, 0.09833610648918469) (0.0460359568992712, 0.10095589468388395)
-(0.04358612933479947, 0.09564489112227806) (0.04261383273870651, 0.09507042253521127)
-(0.04128931422858646, 0.09077256799599533) (0.042503272384162856, 0.09485368314833502)
-(0.043970261843005794, 0.09475096427972497) (0.042389636396742716, 0.09444351743700985)
-(0.045238677585835635, 0.0971150620597115) (0.044671639993329695, 0.09896528704939919)
-(0.04468767124491524, 0.09563025210084034) (0.044465484587960555, 0.09803273268308811)
-(0.04545421956093857, 0.09889926617745164) (0.04260071358855361, 0.09299213915370463)
-(0.04698144298129544, 0.09924812030075188) (0.0445872303203614, 0.09728544933400776)
-(0.04698439506478846, 0.10084033613445378) (0.04227566283607216, 0.0933780385582565)
-(0.04686287022782827, 0.09790444258172674) (0.04093423738573832, 0.08991962491627595)
-(0.04207474937499147, 0.09171105193075899) (0.044001042677243744, 0.09548494983277592)
-(0.04349252527387824, 0.09087899354411522) (0.04532052084971128, 0.10125936010891763)
-(0.04504383099253152, 0.09564346519779669) (0.04471522343514236, 0.09889926617745164)
-(0.04453584742104499, 0.09655402030963875) (0.043379123373391656, 0.09518233522917363)
-(0.04734798778587445, 0.09855849815621857) (0.04627600879854919, 0.10107274555816292)
-(0.046065615041243395, 0.09935680433310765) (0.046580859195749905, 0.10055583628094998)
-(0.04310740337516884, 0.09306599832915623) (0.04411939335473692, 0.0962182061579652)
-(0.04502056531710858, 0.09503784693019345) (0.04284339779169671, 0.09645010046885466)
-(0.043917445943709575, 0.09433333333333334) (0.04107515513937971, 0.09063444108761329)
-(0.04560131349859057, 0.09773441375888871) (0.04194879222157747, 0.09536556801070771)
-(0.04851305172714049, 0.10114980836527246) (0.0435360059908385, 0.0948073701842546)
-(0.04267579754388378, 0.09437113746450643) (0.04473541662223114, 0.09934662422516334)
-(0.04798602951741334, 0.10063417890520694) (0.042088276239164016, 0.09445551128818061)
-(0.04453145229737061, 0.09627277285642655) (0.04296047526336265, 0.09382924767540152)
-(0.043139343527128796, 0.09169638308711156) (0.042095087278438165, 0.09701742627345844)
diff --git a/SASRec/test.ipynb b/SASRec/test.ipynb
index 1fcf76a..9eef931 100644
--- a/SASRec/test.ipynb
+++ b/SASRec/test.ipynb
@@ -1,5162 +1 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Collecting pymysql\n",
-      "  Downloading PyMySQL-1.1.0-py3-none-any.whl (44 kB)\n",
-      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m44.8/44.8 kB\u001B[0m \u001B[31m504.4 kB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
-      "\u001B[?25hInstalling collected packages: pymysql\n",
-      "Successfully installed pymysql-1.1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "pip install pymysql"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "NKDyPANDcj7D",
-    "executionInfo": {
-     "status": "ok",
-     "timestamp": 1703384850058,
-     "user_tz": -540,
-     "elapsed": 13070,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     }
-    },
-    "outputId": "8ff71f23-074d-4684-afc8-011872a3740d",
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "outputs": [],
-   "source": [
-    "import pymysql"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "WVE63KBbcj7D",
-    "executionInfo": {
-     "status": "ok",
-     "timestamp": 1703384850059,
-     "user_tz": -540,
-     "elapsed": 13,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     }
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "outputs": [],
-   "source": [
-    "endpoint = \"pseudorec.cvhv2t0obyv3.ap-northeast-2.rds.amazonaws.com\"\n",
-    "port = 3306\n",
-    "user = \"admin\"\n",
-    "region = \"ap-northeast-2c\"\n",
-    "dbname = \"movielens25m\"\n",
-    "passwd = 'Precsys1!'\n",
-    "\n",
-    "# connection = pymysql.connect(host=endpoint, user=user, passwd=passwd, port=port,\n",
-    "#                                 database=dbname)"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "xZRkRRYucj7E",
-    "executionInfo": {
-     "status": "ok",
-     "timestamp": 1703384850059,
-     "user_tz": -540,
-     "elapsed": 10,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     }
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Mounted at /content/drive\n"
-     ]
-    }
-   ],
-   "source": [
-    "from google.colab import drive\n",
-    "drive.mount('/content/drive')"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "nwzAtR3Ecj7E",
-    "executionInfo": {
-     "status": "ok",
-     "timestamp": 1703384870906,
-     "user_tz": -540,
-     "elapsed": 20856,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     }
-    },
-    "outputId": "4098d371-17ce-483b-d1b1-5b6353f30d81"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "'/content'"
-      ],
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "type": "string"
-      }
-     },
-     "metadata": {},
-     "execution_count": 6
-    }
-   ],
-   "source": [
-    "import os\n",
-    "os.getcwd()"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 35
-    },
-    "id": "jLYz0Wx9cj7E",
-    "executionInfo": {
-     "status": "ok",
-     "timestamp": 1703384875655,
-     "user_tz": -540,
-     "elapsed": 31,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     }
-    },
-    "outputId": "11ccdb2a-ef57-43b4-c9e0-d51900540881"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.chdir('/content/drive/MyDrive/000GithubRepos/recsys_key_papers_implementation/SASRec')"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "uF4rBkGgcj7E",
-    "executionInfo": {
-     "status": "ok",
-     "timestamp": 1703384876035,
-     "user_tz": -540,
-     "elapsed": 406,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     }
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "wqdPtiQucj7E",
-    "executionInfo": {
-     "status": "ok",
-     "timestamp": 1703384876410,
-     "user_tz": -540,
-     "elapsed": 383,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     }
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "['/content',\n",
-       " '/env/python',\n",
-       " '/usr/lib/python310.zip',\n",
-       " '/usr/lib/python3.10',\n",
-       " '/usr/lib/python3.10/lib-dynload',\n",
-       " '',\n",
-       " '/usr/local/lib/python3.10/dist-packages',\n",
-       " '/usr/lib/python3/dist-packages',\n",
-       " '/usr/local/lib/python3.10/dist-packages/IPython/extensions',\n",
-       " '/root/.ipython']"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 9
-    }
-   ],
-   "source": [
-    "import sys\n",
-    "sys.path"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "e7emrLYncj7E",
-    "executionInfo": {
-     "status": "ok",
-     "timestamp": 1703384876410,
-     "user_tz": -540,
-     "elapsed": 18,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     }
-    },
-    "outputId": "0217a2a5-d687-48dd-8c75-fe34dffaa8fa"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "outputs": [],
-   "source": [
-    "sys.path.append('../')"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "QNnGfwwmcj7E",
-    "executionInfo": {
-     "status": "ok",
-     "timestamp": 1703384876410,
-     "user_tz": -540,
-     "elapsed": 14,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     }
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Collecting boto3\n",
-      "  Downloading boto3-1.34.7-py3-none-any.whl (139 kB)\n",
-      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m139.3/139.3 kB\u001B[0m \u001B[31m1.2 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
-      "\u001B[?25h"
-     ]
-    }
-   ],
-   "source": [
-    "pip install boto3"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "tqYg8k9_cj7E",
-    "outputId": "63c90e3d-9500-4898-ca5e-e5d60cc23770",
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# 데이터 불러오기"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    },
-    "id": "KV0gagCTcj7E"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "import boto3\n",
-    "import pandas as pd\n",
-    "import pymysql\n",
-    "from boto3.dynamodb.conditions import Key\n",
-    "# from dotenv import load_dotenv\n",
-    "\n",
-    "# load_dotenv()\n",
-    "\n",
-    "class MysqlClient:\n",
-    "    def __init__(self):\n",
-    "        self.endpoint = \"pseudorec.cvhv2t0obyv3.ap-northeast-2.rds.amazonaws.com\"\n",
-    "        self.port = 3306\n",
-    "        self.user = \"admin\"\n",
-    "        self.region = \"ap-northeast-2c\"\n",
-    "        self.dbname = \"movielens25m\"\n",
-    "        self.passwd = 'Precsys1!'\n",
-    "        os.environ['LIBMYSQL_ENABLE_CLEARTEXT_PLUGIN'] = '1'\n",
-    "        # self.connection = pymysql.connect(host=endpoint, user=user, passwd=passwd, port=port, database=dbname)\n",
-    "\n",
-    "    def get_connection(self):\n",
-    "        connection = pymysql.connect(host=self.endpoint, user=self.user, passwd=self.passwd, port=self.port,\n",
-    "                                     database=self.dbname)\n",
-    "        return connection\n",
-    "\n",
-    "    def get_count(self, table_name):\n",
-    "        with self.get_connection().cursor() as cursor:\n",
-    "            cursor.execute(f\"select count(*) from {table_name}\")\n",
-    "            return cursor.fetchall()[0][0]\n",
-    "\n",
-    "    def get_movies(self):\n",
-    "        with self.get_connection() as connection:\n",
-    "            df = pd.read_sql(sql='select * from movies', con=connection)\n",
-    "            return df\n",
-    "\n",
-    "    def get_daum_movies(self):\n",
-    "        with self.get_connection() as connection:\n",
-    "            df = pd.read_sql(sql='select * from daum_movies', con=connection)\n",
-    "            return df\n",
-    "\n",
-    "    def get_daum_ratings(self):\n",
-    "        with self.get_connection() as connection:\n",
-    "            df = pd.read_sql(sql='select * from daum_ratings', con=connection)\n",
-    "            return df\n",
-    "\n",
-    "    def get_url(self, title):\n",
-    "        with self.get_connection() as connection:\n",
-    "            cursor = connection.cursor()\n",
-    "            cursor.execute(f\"\"\"\n",
-    "            select url from movies where title = '{title}'\n",
-    "            \"\"\")\n",
-    "            url = cursor.fetchall()[0][0]\n",
-    "            return url\n",
-    "\n",
-    "    def get_table_names(self):\n",
-    "        print(\"Tables : \")\n",
-    "        with self.get_connection().cursor() as cursor:\n",
-    "            sql = \"SHOW TABLES\"\n",
-    "            cursor.execute(sql)\n",
-    "            result = cursor.fetchall()\n",
-    "            for row in result:\n",
-    "                print(row[0])\n",
-    "\n",
-    "    def get_data_type(self, table_name):\n",
-    "        with self.get_connection().cursor() as cursor:\n",
-    "            cursor.execute(f\"SHOW COLUMNS FROM {table_name}\")\n",
-    "            columns = cursor.fetchall()\n",
-    "        for column in columns:\n",
-    "            column_name = column[0]\n",
-    "            data_type = column[1]\n",
-    "            print(f\"Column: {column_name}, Data Type: {data_type}\")\n",
-    "\n",
-    "\n",
-    "class DynamoDB:\n",
-    "    def __init__(self, table_name: str):\n",
-    "        self.resource = boto3.resource(\n",
-    "            'dynamodb',\n",
-    "            aws_access_key_id=os.environ['AWS_ACCESS_KEY_ID'],\n",
-    "            aws_secret_access_key=os.environ['AWS_SECRET_ACCESS_KEY'],\n",
-    "            region_name=os.environ['AWS_REGION_NAME'],\n",
-    "        )\n",
-    "\n",
-    "        self.client = boto3.client(\n",
-    "            'dynamodb',\n",
-    "            aws_access_key_id=os.environ['AWS_ACCESS_KEY_ID'],\n",
-    "            aws_secret_access_key=os.environ['AWS_SECRET_ACCESS_KEY'],\n",
-    "            region_name=os.environ['AWS_REGION_NAME'],\n",
-    "        )\n",
-    "        self.table = self.resource.Table(table_name)  # clicklog 테이블 등으로 연결\n",
-    "\n",
-    "    def put_item(self, click_log):\n",
-    "        resp = self.table.put_item(Item=click_log)\n",
-    "\n",
-    "    def get_a_user_logs(self, user_name: str):\n",
-    "        query = {\"KeyConditionExpression\": Key(\"userId\").eq(user_name)}\n",
-    "        resp = self.table.query(**query)\n",
-    "        return pd.DataFrame(resp['Items'])\n"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "akF5br0Dcj7E"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "mysql = MysqlClient()"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "w7_oitsecj7E"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "<ipython-input-50-22af03e4e8d4>:39: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
-      "  df = pd.read_sql(sql='select * from daum_movies', con=connection)\n"
-     ]
-    }
-   ],
-   "source": [
-    "daum_movies = mysql.get_daum_movies()"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "z4smMQdpcj7E",
-    "executionInfo": {
-     "status": "ok",
-     "timestamp": 1703381652208,
-     "user_tz": -540,
-     "elapsed": 1164,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     }
-    },
-    "outputId": "7e163257-52a4-405a-d063-e241cd78c5b0"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "       movieId            titleKo  \\\n",
-       "0            1            플란다스의 개   \n",
-       "1            2                 카라   \n",
-       "2            3           주노명 베이커리   \n",
-       "3            4       여고괴담 두번째 이야기   \n",
-       "4            5            행복한 장의사   \n",
-       "...        ...                ...   \n",
-       "26777   175738                 파묘   \n",
-       "26778   175807  고질라 x 콩: 더 뉴 엠파이어   \n",
-       "26779   175838              장안궤사전   \n",
-       "26780   175990   실버 앤드 더 북 오브 드림스   \n",
-       "26781   176148              쿵푸팬더4   \n",
-       "\n",
-       "                                                 titleEn  \\\n",
-       "0                           Barking Dogs Never Bite 2000   \n",
-       "1                                             Calla 1999   \n",
-       "2                                                   2000   \n",
-       "3                                      Memento Mori 1999   \n",
-       "4                                                   2000   \n",
-       "...                                                  ...   \n",
-       "26777                                               2022   \n",
-       "26778               Godzilla x Kong: The New Empire 2024   \n",
-       "26779                        The Man's Secret 長安詭事傳 2023   \n",
-       "26780  Silver and the Book of Dreams Silber und das B...   \n",
-       "26781                               Kung Fu Panda 4 2024   \n",
-       "\n",
-       "                                             mainPageUrl  \\\n",
-       "0         https://movie.daum.net/moviedb/grade?movieId=1   \n",
-       "1         https://movie.daum.net/moviedb/grade?movieId=2   \n",
-       "2         https://movie.daum.net/moviedb/grade?movieId=3   \n",
-       "3         https://movie.daum.net/moviedb/grade?movieId=4   \n",
-       "4         https://movie.daum.net/moviedb/grade?movieId=5   \n",
-       "...                                                  ...   \n",
-       "26777  https://movie.daum.net/moviedb/grade?movieId=1...   \n",
-       "26778  https://movie.daum.net/moviedb/grade?movieId=1...   \n",
-       "26779  https://movie.daum.net/moviedb/grade?movieId=1...   \n",
-       "26780  https://movie.daum.net/moviedb/grade?movieId=1...   \n",
-       "26781  https://movie.daum.net/moviedb/grade?movieId=1...   \n",
-       "\n",
-       "                                               posterUrl  numOfSiteRatings  \n",
-       "0      https://img1.daumcdn.net/thumb/C408x596/?fname...             126.0  \n",
-       "1      https://img1.daumcdn.net/thumb/C408x596/?fname...              23.0  \n",
-       "2                                                   None              15.0  \n",
-       "3      https://img1.daumcdn.net/thumb/C408x596/?fname...              74.0  \n",
-       "4      https://img1.daumcdn.net/thumb/C408x596/?fname...              53.0  \n",
-       "...                                                  ...               ...  \n",
-       "26777  https://img1.daumcdn.net/thumb/C408x596/?fname...               2.0  \n",
-       "26778  https://img1.daumcdn.net/thumb/C408x596/?fname...               1.0  \n",
-       "26779  https://img1.daumcdn.net/thumb/C408x596/?fname...               3.0  \n",
-       "26780  https://img1.daumcdn.net/thumb/C408x596/?fname...               3.0  \n",
-       "26781  https://img1.daumcdn.net/thumb/C408x596/?fname...               1.0  \n",
-       "\n",
-       "[26782 rows x 6 columns]"
-      ],
-      "text/html": [
-       "\n",
-       "  <div id=\"df-521f2759-6915-412d-aaff-41ab92955017\" class=\"colab-df-container\">\n",
-       "    <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>movieId</th>\n",
-       "      <th>titleKo</th>\n",
-       "      <th>titleEn</th>\n",
-       "      <th>mainPageUrl</th>\n",
-       "      <th>posterUrl</th>\n",
-       "      <th>numOfSiteRatings</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>플란다스의 개</td>\n",
-       "      <td>Barking Dogs Never Bite 2000</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=1</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>126.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>카라</td>\n",
-       "      <td>Calla 1999</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=2</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>23.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>주노명 베이커리</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=3</td>\n",
-       "      <td>None</td>\n",
-       "      <td>15.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>여고괴담 두번째 이야기</td>\n",
-       "      <td>Memento Mori 1999</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=4</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>74.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>행복한 장의사</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=5</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>53.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26777</th>\n",
-       "      <td>175738</td>\n",
-       "      <td>파묘</td>\n",
-       "      <td>2022</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=1...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26778</th>\n",
-       "      <td>175807</td>\n",
-       "      <td>고질라 x 콩: 더 뉴 엠파이어</td>\n",
-       "      <td>Godzilla x Kong: The New Empire 2024</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=1...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26779</th>\n",
-       "      <td>175838</td>\n",
-       "      <td>장안궤사전</td>\n",
-       "      <td>The Man's Secret 長安詭事傳 2023</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=1...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26780</th>\n",
-       "      <td>175990</td>\n",
-       "      <td>실버 앤드 더 북 오브 드림스</td>\n",
-       "      <td>Silver and the Book of Dreams Silber und das B...</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=1...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26781</th>\n",
-       "      <td>176148</td>\n",
-       "      <td>쿵푸팬더4</td>\n",
-       "      <td>Kung Fu Panda 4 2024</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=1...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>26782 rows × 6 columns</p>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-521f2759-6915-412d-aaff-41ab92955017')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-521f2759-6915-412d-aaff-41ab92955017 button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-521f2759-6915-412d-aaff-41ab92955017');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "<div id=\"df-2984936e-38d4-44d0-8de9-eab743f81fbd\">\n",
-       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2984936e-38d4-44d0-8de9-eab743f81fbd')\"\n",
-       "            title=\"Suggest charts\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "     width=\"24px\">\n",
-       "    <g>\n",
-       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-       "    </g>\n",
-       "</svg>\n",
-       "  </button>\n",
-       "\n",
-       "<style>\n",
-       "  .colab-df-quickchart {\n",
-       "      --bg-color: #E8F0FE;\n",
-       "      --fill-color: #1967D2;\n",
-       "      --hover-bg-color: #E2EBFA;\n",
-       "      --hover-fill-color: #174EA6;\n",
-       "      --disabled-fill-color: #AAA;\n",
-       "      --disabled-bg-color: #DDD;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart {\n",
-       "      --bg-color: #3B4455;\n",
-       "      --fill-color: #D2E3FC;\n",
-       "      --hover-bg-color: #434B5C;\n",
-       "      --hover-fill-color: #FFFFFF;\n",
-       "      --disabled-bg-color: #3B4455;\n",
-       "      --disabled-fill-color: #666;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart {\n",
-       "    background-color: var(--bg-color);\n",
-       "    border: none;\n",
-       "    border-radius: 50%;\n",
-       "    cursor: pointer;\n",
-       "    display: none;\n",
-       "    fill: var(--fill-color);\n",
-       "    height: 32px;\n",
-       "    padding: 0;\n",
-       "    width: 32px;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart:hover {\n",
-       "    background-color: var(--hover-bg-color);\n",
-       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "    fill: var(--button-hover-fill-color);\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart-complete:disabled,\n",
-       "  .colab-df-quickchart-complete:disabled:hover {\n",
-       "    background-color: var(--disabled-bg-color);\n",
-       "    fill: var(--disabled-fill-color);\n",
-       "    box-shadow: none;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-spinner {\n",
-       "    border: 2px solid var(--fill-color);\n",
-       "    border-color: transparent;\n",
-       "    border-bottom-color: var(--fill-color);\n",
-       "    animation:\n",
-       "      spin 1s steps(1) infinite;\n",
-       "  }\n",
-       "\n",
-       "  @keyframes spin {\n",
-       "    0% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "      border-left-color: var(--fill-color);\n",
-       "    }\n",
-       "    20% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    30% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    40% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    60% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    80% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "    90% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "  }\n",
-       "</style>\n",
-       "\n",
-       "  <script>\n",
-       "    async function quickchart(key) {\n",
-       "      const quickchartButtonEl =\n",
-       "        document.querySelector('#' + key + ' button');\n",
-       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-       "      try {\n",
-       "        const charts = await google.colab.kernel.invokeFunction(\n",
-       "            'suggestCharts', [key], {});\n",
-       "      } catch (error) {\n",
-       "        console.error('Error during call to suggestCharts:', error);\n",
-       "      }\n",
-       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-       "    }\n",
-       "    (() => {\n",
-       "      let quickchartButtonEl =\n",
-       "        document.querySelector('#df-2984936e-38d4-44d0-8de9-eab743f81fbd button');\n",
-       "      quickchartButtonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "    })();\n",
-       "  </script>\n",
-       "</div>\n",
-       "\n",
-       "  <div id=\"id_ed9b70ad-82ad-416b-b4b6-a8a8a2b9f8ae\">\n",
-       "    <style>\n",
-       "      .colab-df-generate {\n",
-       "        background-color: #E8F0FE;\n",
-       "        border: none;\n",
-       "        border-radius: 50%;\n",
-       "        cursor: pointer;\n",
-       "        display: none;\n",
-       "        fill: #1967D2;\n",
-       "        height: 32px;\n",
-       "        padding: 0 0 0 0;\n",
-       "        width: 32px;\n",
-       "      }\n",
-       "\n",
-       "      .colab-df-generate:hover {\n",
-       "        background-color: #E2EBFA;\n",
-       "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "        fill: #174EA6;\n",
-       "      }\n",
-       "\n",
-       "      [theme=dark] .colab-df-generate {\n",
-       "        background-color: #3B4455;\n",
-       "        fill: #D2E3FC;\n",
-       "      }\n",
-       "\n",
-       "      [theme=dark] .colab-df-generate:hover {\n",
-       "        background-color: #434B5C;\n",
-       "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "        fill: #FFFFFF;\n",
-       "      }\n",
-       "    </style>\n",
-       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('daum_movies')\"\n",
-       "            title=\"Generate code using this dataframe.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "       width=\"24px\">\n",
-       "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "    <script>\n",
-       "      (() => {\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#id_ed9b70ad-82ad-416b-b4b6-a8a8a2b9f8ae button.colab-df-generate');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      buttonEl.onclick = () => {\n",
-       "        google.colab.notebook.generateWithVariable('daum_movies');\n",
-       "      }\n",
-       "      })();\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 53
-    }
-   ],
-   "source": [
-    "daum_movies"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 597
-    },
-    "id": "osx4U0ZTcj7F",
-    "executionInfo": {
-     "status": "ok",
-     "timestamp": 1703381652212,
-     "user_tz": -540,
-     "elapsed": 16,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     }
-    },
-    "outputId": "f3acbef9-7b91-4581-9d3a-432b3901b7c3"
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# 테이블 총 개수"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    },
-    "id": "LLdujZ3icj7G"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "26782"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 54
-    }
-   ],
-   "source": [
-    "mysql.get_count('daum_movies')"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "dH0HF-9mcj7G",
-    "executionInfo": {
-     "status": "ok",
-     "timestamp": 1703381656460,
-     "user_tz": -540,
-     "elapsed": 498,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     }
-    },
-    "outputId": "96c765ac-75eb-4dd9-cccc-efb2d38f1756"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "381601"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 55
-    }
-   ],
-   "source": [
-    "mysql.get_count('daum_ratings')"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "6ESUGsDMcj7G",
-    "executionInfo": {
-     "status": "ok",
-     "timestamp": 1703381656461,
-     "user_tz": -540,
-     "elapsed": 13,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     }
-    },
-    "outputId": "5a4ec8c6-4709-440e-cdff-fe78511cff56"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "<ipython-input-50-22af03e4e8d4>:44: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
-      "  df = pd.read_sql(sql='select * from daum_ratings', con=connection)\n"
-     ]
-    }
-   ],
-   "source": [
-    "daum_ratings = mysql.get_daum_ratings()"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "FiIIbDCacj7G",
-    "executionInfo": {
-     "status": "ok",
-     "timestamp": 1703381664690,
-     "user_tz": -540,
-     "elapsed": 8236,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     }
-    },
-    "outputId": "4c854bb6-23c2-4b51-8255-63901a6660e1"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "       nickName  movieId  rating     timestamp userId  \\\n",
-       "0         매력적그녀   121721       8  1.702394e+09   None   \n",
-       "1         매력적그녀     1660      10  1.701700e+09   None   \n",
-       "2         매력적그녀   164920      10  1.692027e+09   None   \n",
-       "3         매력적그녀   130710       1  1.684073e+09   None   \n",
-       "4         매력적그녀     1953       6  1.677165e+09   None   \n",
-       "...         ...      ...     ...           ...    ...   \n",
-       "381596     사커홀릭   130908       1  1.636721e+09   None   \n",
-       "381597     사커홀릭   129413      10  1.600674e+09   None   \n",
-       "381598     사커홀릭   122091      10  1.580734e+09   None   \n",
-       "381599     사커홀릭    54520      10  1.560259e+09   None   \n",
-       "381600   휴면 사용자    47747       6  1.469438e+09   None   \n",
-       "\n",
-       "                                                   review  \n",
-       "0       이런 영화 안좋아하는데 어쩔수 없이 봤다가 솔직히 기분은 진짜 별로였음 근데 또 영...  \n",
-       "1       첨엔 내가 주인공이고 이렇게 술술 풀리는 행복한 인생 부럽다 이러다가 갈수록 숨막히...  \n",
-       "2       처음엔 넷플에.떴길래 별 생각없이 보기 시작했는데 너무 잔잔해서 뭐지 하다가 이상하...  \n",
-       "3                                         이거 왜 영화로 만든건가요?  \n",
-       "4       당시엔 평이 엄청 낮고 여주 혹평이던 영화도 시간이 지나면 이렇게 평점이 좋아지는구...  \n",
-       "...                                                   ...  \n",
-       "381596                    보다가 졸음이 온 첫 마블영화. 왜구 원폭피해국 홍보영화  \n",
-       "381597  최고입니다. 누군가들에게는 불편할 듯 ... 보는 내내 찜찜하고 불편했지만 현실세계...  \n",
-       "381598        연기자들의 연기에 찬사를 드립니다~. 다음번엔 궁정동의 여인들도 만들어주세요!  \n",
-       "381599  힐링하고 싶을때 꺼내봅니다. 볼 때마다 놓쳤던 부분을 발견하며 감동이 더하네요. 월...  \n",
-       "381600                                        진부한 복제와 속임수  \n",
-       "\n",
-       "[381601 rows x 6 columns]"
-      ],
-      "text/html": [
-       "\n",
-       "  <div id=\"df-d266a7de-60e9-4a95-b76b-0375373dd46c\" class=\"colab-df-container\">\n",
-       "    <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>nickName</th>\n",
-       "      <th>movieId</th>\n",
-       "      <th>rating</th>\n",
-       "      <th>timestamp</th>\n",
-       "      <th>userId</th>\n",
-       "      <th>review</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>매력적그녀</td>\n",
-       "      <td>121721</td>\n",
-       "      <td>8</td>\n",
-       "      <td>1.702394e+09</td>\n",
-       "      <td>None</td>\n",
-       "      <td>이런 영화 안좋아하는데 어쩔수 없이 봤다가 솔직히 기분은 진짜 별로였음 근데 또 영...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>매력적그녀</td>\n",
-       "      <td>1660</td>\n",
-       "      <td>10</td>\n",
-       "      <td>1.701700e+09</td>\n",
-       "      <td>None</td>\n",
-       "      <td>첨엔 내가 주인공이고 이렇게 술술 풀리는 행복한 인생 부럽다 이러다가 갈수록 숨막히...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>매력적그녀</td>\n",
-       "      <td>164920</td>\n",
-       "      <td>10</td>\n",
-       "      <td>1.692027e+09</td>\n",
-       "      <td>None</td>\n",
-       "      <td>처음엔 넷플에.떴길래 별 생각없이 보기 시작했는데 너무 잔잔해서 뭐지 하다가 이상하...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>매력적그녀</td>\n",
-       "      <td>130710</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.684073e+09</td>\n",
-       "      <td>None</td>\n",
-       "      <td>이거 왜 영화로 만든건가요?</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>매력적그녀</td>\n",
-       "      <td>1953</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1.677165e+09</td>\n",
-       "      <td>None</td>\n",
-       "      <td>당시엔 평이 엄청 낮고 여주 혹평이던 영화도 시간이 지나면 이렇게 평점이 좋아지는구...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>381596</th>\n",
-       "      <td>사커홀릭</td>\n",
-       "      <td>130908</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.636721e+09</td>\n",
-       "      <td>None</td>\n",
-       "      <td>보다가 졸음이 온 첫 마블영화. 왜구 원폭피해국 홍보영화</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>381597</th>\n",
-       "      <td>사커홀릭</td>\n",
-       "      <td>129413</td>\n",
-       "      <td>10</td>\n",
-       "      <td>1.600674e+09</td>\n",
-       "      <td>None</td>\n",
-       "      <td>최고입니다. 누군가들에게는 불편할 듯 ... 보는 내내 찜찜하고 불편했지만 현실세계...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>381598</th>\n",
-       "      <td>사커홀릭</td>\n",
-       "      <td>122091</td>\n",
-       "      <td>10</td>\n",
-       "      <td>1.580734e+09</td>\n",
-       "      <td>None</td>\n",
-       "      <td>연기자들의 연기에 찬사를 드립니다~. 다음번엔 궁정동의 여인들도 만들어주세요!</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>381599</th>\n",
-       "      <td>사커홀릭</td>\n",
-       "      <td>54520</td>\n",
-       "      <td>10</td>\n",
-       "      <td>1.560259e+09</td>\n",
-       "      <td>None</td>\n",
-       "      <td>힐링하고 싶을때 꺼내봅니다. 볼 때마다 놓쳤던 부분을 발견하며 감동이 더하네요. 월...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>381600</th>\n",
-       "      <td>휴면 사용자</td>\n",
-       "      <td>47747</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1.469438e+09</td>\n",
-       "      <td>None</td>\n",
-       "      <td>진부한 복제와 속임수</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>381601 rows × 6 columns</p>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d266a7de-60e9-4a95-b76b-0375373dd46c')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-d266a7de-60e9-4a95-b76b-0375373dd46c button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-d266a7de-60e9-4a95-b76b-0375373dd46c');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "<div id=\"df-971b890f-fb16-4068-b1d2-61c4120f6cdc\">\n",
-       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-971b890f-fb16-4068-b1d2-61c4120f6cdc')\"\n",
-       "            title=\"Suggest charts\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "     width=\"24px\">\n",
-       "    <g>\n",
-       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-       "    </g>\n",
-       "</svg>\n",
-       "  </button>\n",
-       "\n",
-       "<style>\n",
-       "  .colab-df-quickchart {\n",
-       "      --bg-color: #E8F0FE;\n",
-       "      --fill-color: #1967D2;\n",
-       "      --hover-bg-color: #E2EBFA;\n",
-       "      --hover-fill-color: #174EA6;\n",
-       "      --disabled-fill-color: #AAA;\n",
-       "      --disabled-bg-color: #DDD;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart {\n",
-       "      --bg-color: #3B4455;\n",
-       "      --fill-color: #D2E3FC;\n",
-       "      --hover-bg-color: #434B5C;\n",
-       "      --hover-fill-color: #FFFFFF;\n",
-       "      --disabled-bg-color: #3B4455;\n",
-       "      --disabled-fill-color: #666;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart {\n",
-       "    background-color: var(--bg-color);\n",
-       "    border: none;\n",
-       "    border-radius: 50%;\n",
-       "    cursor: pointer;\n",
-       "    display: none;\n",
-       "    fill: var(--fill-color);\n",
-       "    height: 32px;\n",
-       "    padding: 0;\n",
-       "    width: 32px;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart:hover {\n",
-       "    background-color: var(--hover-bg-color);\n",
-       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "    fill: var(--button-hover-fill-color);\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart-complete:disabled,\n",
-       "  .colab-df-quickchart-complete:disabled:hover {\n",
-       "    background-color: var(--disabled-bg-color);\n",
-       "    fill: var(--disabled-fill-color);\n",
-       "    box-shadow: none;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-spinner {\n",
-       "    border: 2px solid var(--fill-color);\n",
-       "    border-color: transparent;\n",
-       "    border-bottom-color: var(--fill-color);\n",
-       "    animation:\n",
-       "      spin 1s steps(1) infinite;\n",
-       "  }\n",
-       "\n",
-       "  @keyframes spin {\n",
-       "    0% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "      border-left-color: var(--fill-color);\n",
-       "    }\n",
-       "    20% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    30% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    40% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    60% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    80% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "    90% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "  }\n",
-       "</style>\n",
-       "\n",
-       "  <script>\n",
-       "    async function quickchart(key) {\n",
-       "      const quickchartButtonEl =\n",
-       "        document.querySelector('#' + key + ' button');\n",
-       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-       "      try {\n",
-       "        const charts = await google.colab.kernel.invokeFunction(\n",
-       "            'suggestCharts', [key], {});\n",
-       "      } catch (error) {\n",
-       "        console.error('Error during call to suggestCharts:', error);\n",
-       "      }\n",
-       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-       "    }\n",
-       "    (() => {\n",
-       "      let quickchartButtonEl =\n",
-       "        document.querySelector('#df-971b890f-fb16-4068-b1d2-61c4120f6cdc button');\n",
-       "      quickchartButtonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "    })();\n",
-       "  </script>\n",
-       "</div>\n",
-       "\n",
-       "  <div id=\"id_248ee35b-e37d-4de5-acc4-6024ee9e4f5c\">\n",
-       "    <style>\n",
-       "      .colab-df-generate {\n",
-       "        background-color: #E8F0FE;\n",
-       "        border: none;\n",
-       "        border-radius: 50%;\n",
-       "        cursor: pointer;\n",
-       "        display: none;\n",
-       "        fill: #1967D2;\n",
-       "        height: 32px;\n",
-       "        padding: 0 0 0 0;\n",
-       "        width: 32px;\n",
-       "      }\n",
-       "\n",
-       "      .colab-df-generate:hover {\n",
-       "        background-color: #E2EBFA;\n",
-       "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "        fill: #174EA6;\n",
-       "      }\n",
-       "\n",
-       "      [theme=dark] .colab-df-generate {\n",
-       "        background-color: #3B4455;\n",
-       "        fill: #D2E3FC;\n",
-       "      }\n",
-       "\n",
-       "      [theme=dark] .colab-df-generate:hover {\n",
-       "        background-color: #434B5C;\n",
-       "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "        fill: #FFFFFF;\n",
-       "      }\n",
-       "    </style>\n",
-       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('daum_ratings')\"\n",
-       "            title=\"Generate code using this dataframe.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "       width=\"24px\">\n",
-       "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "    <script>\n",
-       "      (() => {\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#id_248ee35b-e37d-4de5-acc4-6024ee9e4f5c button.colab-df-generate');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      buttonEl.onclick = () => {\n",
-       "        google.colab.notebook.generateWithVariable('daum_ratings');\n",
-       "      }\n",
-       "      })();\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 57
-    }
-   ],
-   "source": [
-    "daum_ratings"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 424
-    },
-    "id": "SFUhJn8lcj7G",
-    "executionInfo": {
-     "status": "ok",
-     "timestamp": 1703381664691,
-     "user_tz": -540,
-     "elapsed": 28,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     }
-    },
-    "outputId": "07520091-3c9b-48f1-b946-3364d792d059"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "nickName      23894\n",
-       "movieId       26636\n",
-       "rating           11\n",
-       "timestamp    362438\n",
-       "userId            0\n",
-       "review       356470\n",
-       "dtype: int64"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 58
-    }
-   ],
-   "source": [
-    "daum_ratings.nunique()"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "2XGHWn-bcj7G",
-    "executionInfo": {
-     "status": "ok",
-     "timestamp": 1703381664692,
-     "user_tz": -540,
-     "elapsed": 24,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     }
-    },
-    "outputId": "b4c9f1ae-2bf5-46fd-99f0-e8b34900559a"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "from collections import Counter\n",
-    "nn_cntr = Counter(daum_ratings['nickName'])"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "ell1p1Mhcj7G"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[('휴면 사용자', 10798),\n",
-       " ('', 2731),\n",
-       " ('닉네임을 등록해 주세요', 1998),\n",
-       " ('붉은돼지', 680),\n",
-       " ('이리', 631),\n",
-       " ('koh501', 630),\n",
-       " ('버닝샌더스', 630),\n",
-       " ('dArKRuSh', 630),\n",
-       " ('Peter_L', 630),\n",
-       " ('달빛의그림자', 630),\n",
-       " ('빨간다라이', 630),\n",
-       " ('하늘바람별', 630),\n",
-       " ('이상', 630),\n",
-       " ('뒤마페르', 629),\n",
-       " ('Tough Cookie', 629),\n",
-       " ('잉여인간', 628),\n",
-       " ('재밌는놀이', 628),\n",
-       " ('야수죽이기', 627),\n",
-       " ('김덕뱀다', 564),\n",
-       " ('르네루소', 562),\n",
-       " ('L_H_K_', 552),\n",
-       " ('땅콩샌드', 547),\n",
-       " ('나는 단호하게 반대한다', 539),\n",
-       " ('다함께', 527),\n",
-       " ('김동혁', 523),\n",
-       " ('억수로', 465),\n",
-       " ('이지숙', 458),\n",
-       " ('즐거운인생', 444),\n",
-       " ('one', 412),\n",
-       " ('닉네임', 395),\n",
-       " ('하바별시', 387),\n",
-       " ('아다나', 385),\n",
-       " ('탁선생', 361),\n",
-       " ('지수', 354),\n",
-       " ('tachyon', 341),\n",
-       " ('백호', 339),\n",
-       " ('해명', 337),\n",
-       " ('대한민국', 336),\n",
-       " ('오늘', 334),\n",
-       " ('Freeman', 331),\n",
-       " ('lemmy', 330),\n",
-       " ('즐산', 330),\n",
-       " ('문향서기', 330),\n",
-       " ('Yungeotgom', 330),\n",
-       " ('꽉이', 330),\n",
-       " ('kingofjoy', 330),\n",
-       " ('류관원', 330),\n",
-       " ('감찬', 330),\n",
-       " ('goodmodel', 330),\n",
-       " ('바크', 330)]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 60
-    }
-   ],
-   "source": [
-    "sorted(nn_cntr.items(), key=lambda x: x[1], reverse=True)[:50]"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "d7AyJW3Ocj7G",
-    "executionInfo": {
-     "status": "ok",
-     "timestamp": 1703381670023,
-     "user_tz": -540,
-     "elapsed": 12,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     }
-    },
-    "outputId": "2780f7a8-e99a-496b-a1c6-07a6d34d4aa2"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "daum_movies"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "5C5eF3pocj7G"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "available_user = daum_ratings[daum_ratings['nickName'].map(lambda x: x not in ['휴면 사용자', '', '닉네임을 등록해 주세요', '닉네임'])]"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "t-SF-nnAcj7G"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "       nickName  movieId  rating     timestamp userId  \\\n",
-       "0         매력적그녀   121721       8  1.702394e+09   None   \n",
-       "1         매력적그녀     1660      10  1.701700e+09   None   \n",
-       "2         매력적그녀   164920      10  1.692027e+09   None   \n",
-       "3         매력적그녀   130710       1  1.684073e+09   None   \n",
-       "4         매력적그녀     1953       6  1.677165e+09   None   \n",
-       "...         ...      ...     ...           ...    ...   \n",
-       "381595      참고로    93503       8  1.498874e+09   None   \n",
-       "381596     사커홀릭   130908       1  1.636721e+09   None   \n",
-       "381597     사커홀릭   129413      10  1.600674e+09   None   \n",
-       "381598     사커홀릭   122091      10  1.580734e+09   None   \n",
-       "381599     사커홀릭    54520      10  1.560259e+09   None   \n",
-       "\n",
-       "                                                   review  \n",
-       "0       이런 영화 안좋아하는데 어쩔수 없이 봤다가 솔직히 기분은 진짜 별로였음 근데 또 영...  \n",
-       "1       첨엔 내가 주인공이고 이렇게 술술 풀리는 행복한 인생 부럽다 이러다가 갈수록 숨막히...  \n",
-       "2       처음엔 넷플에.떴길래 별 생각없이 보기 시작했는데 너무 잔잔해서 뭐지 하다가 이상하...  \n",
-       "3                                         이거 왜 영화로 만든건가요?  \n",
-       "4       당시엔 평이 엄청 낮고 여주 혹평이던 영화도 시간이 지나면 이렇게 평점이 좋아지는구...  \n",
-       "...                                                   ...  \n",
-       "381595                                            데이빗의 변   \n",
-       "381596                    보다가 졸음이 온 첫 마블영화. 왜구 원폭피해국 홍보영화  \n",
-       "381597  최고입니다. 누군가들에게는 불편할 듯 ... 보는 내내 찜찜하고 불편했지만 현실세계...  \n",
-       "381598        연기자들의 연기에 찬사를 드립니다~. 다음번엔 궁정동의 여인들도 만들어주세요!  \n",
-       "381599  힐링하고 싶을때 꺼내봅니다. 볼 때마다 놓쳤던 부분을 발견하며 감동이 더하네요. 월...  \n",
-       "\n",
-       "[365679 rows x 6 columns]"
-      ],
-      "text/html": [
-       "\n",
-       "  <div id=\"df-475334a9-7509-4d33-9b8f-5bd6610b76da\" class=\"colab-df-container\">\n",
-       "    <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>nickName</th>\n",
-       "      <th>movieId</th>\n",
-       "      <th>rating</th>\n",
-       "      <th>timestamp</th>\n",
-       "      <th>userId</th>\n",
-       "      <th>review</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>매력적그녀</td>\n",
-       "      <td>121721</td>\n",
-       "      <td>8</td>\n",
-       "      <td>1.702394e+09</td>\n",
-       "      <td>None</td>\n",
-       "      <td>이런 영화 안좋아하는데 어쩔수 없이 봤다가 솔직히 기분은 진짜 별로였음 근데 또 영...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>매력적그녀</td>\n",
-       "      <td>1660</td>\n",
-       "      <td>10</td>\n",
-       "      <td>1.701700e+09</td>\n",
-       "      <td>None</td>\n",
-       "      <td>첨엔 내가 주인공이고 이렇게 술술 풀리는 행복한 인생 부럽다 이러다가 갈수록 숨막히...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>매력적그녀</td>\n",
-       "      <td>164920</td>\n",
-       "      <td>10</td>\n",
-       "      <td>1.692027e+09</td>\n",
-       "      <td>None</td>\n",
-       "      <td>처음엔 넷플에.떴길래 별 생각없이 보기 시작했는데 너무 잔잔해서 뭐지 하다가 이상하...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>매력적그녀</td>\n",
-       "      <td>130710</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.684073e+09</td>\n",
-       "      <td>None</td>\n",
-       "      <td>이거 왜 영화로 만든건가요?</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>매력적그녀</td>\n",
-       "      <td>1953</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1.677165e+09</td>\n",
-       "      <td>None</td>\n",
-       "      <td>당시엔 평이 엄청 낮고 여주 혹평이던 영화도 시간이 지나면 이렇게 평점이 좋아지는구...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>381595</th>\n",
-       "      <td>참고로</td>\n",
-       "      <td>93503</td>\n",
-       "      <td>8</td>\n",
-       "      <td>1.498874e+09</td>\n",
-       "      <td>None</td>\n",
-       "      <td>데이빗의 변</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>381596</th>\n",
-       "      <td>사커홀릭</td>\n",
-       "      <td>130908</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.636721e+09</td>\n",
-       "      <td>None</td>\n",
-       "      <td>보다가 졸음이 온 첫 마블영화. 왜구 원폭피해국 홍보영화</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>381597</th>\n",
-       "      <td>사커홀릭</td>\n",
-       "      <td>129413</td>\n",
-       "      <td>10</td>\n",
-       "      <td>1.600674e+09</td>\n",
-       "      <td>None</td>\n",
-       "      <td>최고입니다. 누군가들에게는 불편할 듯 ... 보는 내내 찜찜하고 불편했지만 현실세계...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>381598</th>\n",
-       "      <td>사커홀릭</td>\n",
-       "      <td>122091</td>\n",
-       "      <td>10</td>\n",
-       "      <td>1.580734e+09</td>\n",
-       "      <td>None</td>\n",
-       "      <td>연기자들의 연기에 찬사를 드립니다~. 다음번엔 궁정동의 여인들도 만들어주세요!</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>381599</th>\n",
-       "      <td>사커홀릭</td>\n",
-       "      <td>54520</td>\n",
-       "      <td>10</td>\n",
-       "      <td>1.560259e+09</td>\n",
-       "      <td>None</td>\n",
-       "      <td>힐링하고 싶을때 꺼내봅니다. 볼 때마다 놓쳤던 부분을 발견하며 감동이 더하네요. 월...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>365679 rows × 6 columns</p>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-475334a9-7509-4d33-9b8f-5bd6610b76da')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-475334a9-7509-4d33-9b8f-5bd6610b76da button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-475334a9-7509-4d33-9b8f-5bd6610b76da');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "<div id=\"df-dbb3a206-bb44-43b7-94f6-737dd4e2b5c3\">\n",
-       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dbb3a206-bb44-43b7-94f6-737dd4e2b5c3')\"\n",
-       "            title=\"Suggest charts\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "     width=\"24px\">\n",
-       "    <g>\n",
-       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-       "    </g>\n",
-       "</svg>\n",
-       "  </button>\n",
-       "\n",
-       "<style>\n",
-       "  .colab-df-quickchart {\n",
-       "      --bg-color: #E8F0FE;\n",
-       "      --fill-color: #1967D2;\n",
-       "      --hover-bg-color: #E2EBFA;\n",
-       "      --hover-fill-color: #174EA6;\n",
-       "      --disabled-fill-color: #AAA;\n",
-       "      --disabled-bg-color: #DDD;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart {\n",
-       "      --bg-color: #3B4455;\n",
-       "      --fill-color: #D2E3FC;\n",
-       "      --hover-bg-color: #434B5C;\n",
-       "      --hover-fill-color: #FFFFFF;\n",
-       "      --disabled-bg-color: #3B4455;\n",
-       "      --disabled-fill-color: #666;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart {\n",
-       "    background-color: var(--bg-color);\n",
-       "    border: none;\n",
-       "    border-radius: 50%;\n",
-       "    cursor: pointer;\n",
-       "    display: none;\n",
-       "    fill: var(--fill-color);\n",
-       "    height: 32px;\n",
-       "    padding: 0;\n",
-       "    width: 32px;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart:hover {\n",
-       "    background-color: var(--hover-bg-color);\n",
-       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "    fill: var(--button-hover-fill-color);\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart-complete:disabled,\n",
-       "  .colab-df-quickchart-complete:disabled:hover {\n",
-       "    background-color: var(--disabled-bg-color);\n",
-       "    fill: var(--disabled-fill-color);\n",
-       "    box-shadow: none;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-spinner {\n",
-       "    border: 2px solid var(--fill-color);\n",
-       "    border-color: transparent;\n",
-       "    border-bottom-color: var(--fill-color);\n",
-       "    animation:\n",
-       "      spin 1s steps(1) infinite;\n",
-       "  }\n",
-       "\n",
-       "  @keyframes spin {\n",
-       "    0% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "      border-left-color: var(--fill-color);\n",
-       "    }\n",
-       "    20% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    30% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    40% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    60% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    80% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "    90% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "  }\n",
-       "</style>\n",
-       "\n",
-       "  <script>\n",
-       "    async function quickchart(key) {\n",
-       "      const quickchartButtonEl =\n",
-       "        document.querySelector('#' + key + ' button');\n",
-       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-       "      try {\n",
-       "        const charts = await google.colab.kernel.invokeFunction(\n",
-       "            'suggestCharts', [key], {});\n",
-       "      } catch (error) {\n",
-       "        console.error('Error during call to suggestCharts:', error);\n",
-       "      }\n",
-       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-       "    }\n",
-       "    (() => {\n",
-       "      let quickchartButtonEl =\n",
-       "        document.querySelector('#df-dbb3a206-bb44-43b7-94f6-737dd4e2b5c3 button');\n",
-       "      quickchartButtonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "    })();\n",
-       "  </script>\n",
-       "</div>\n",
-       "\n",
-       "  <div id=\"id_d1942086-87de-45c4-90dd-9d2c7e1f9bb3\">\n",
-       "    <style>\n",
-       "      .colab-df-generate {\n",
-       "        background-color: #E8F0FE;\n",
-       "        border: none;\n",
-       "        border-radius: 50%;\n",
-       "        cursor: pointer;\n",
-       "        display: none;\n",
-       "        fill: #1967D2;\n",
-       "        height: 32px;\n",
-       "        padding: 0 0 0 0;\n",
-       "        width: 32px;\n",
-       "      }\n",
-       "\n",
-       "      .colab-df-generate:hover {\n",
-       "        background-color: #E2EBFA;\n",
-       "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "        fill: #174EA6;\n",
-       "      }\n",
-       "\n",
-       "      [theme=dark] .colab-df-generate {\n",
-       "        background-color: #3B4455;\n",
-       "        fill: #D2E3FC;\n",
-       "      }\n",
-       "\n",
-       "      [theme=dark] .colab-df-generate:hover {\n",
-       "        background-color: #434B5C;\n",
-       "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "        fill: #FFFFFF;\n",
-       "      }\n",
-       "    </style>\n",
-       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('available_user')\"\n",
-       "            title=\"Generate code using this dataframe.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "       width=\"24px\">\n",
-       "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "    <script>\n",
-       "      (() => {\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#id_d1942086-87de-45c4-90dd-9d2c7e1f9bb3 button.colab-df-generate');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      buttonEl.onclick = () => {\n",
-       "        google.colab.notebook.generateWithVariable('available_user');\n",
-       "      }\n",
-       "      })();\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 62
-    }
-   ],
-   "source": [
-    "available_user"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 424
-    },
-    "id": "P9qO9yLGcj7G",
-    "executionInfo": {
-     "status": "ok",
-     "timestamp": 1703381715554,
-     "user_tz": -540,
-     "elapsed": 499,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     }
-    },
-    "outputId": "d9349e72-dc64-4c18-b82c-15c279126a60"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "merged = pd.merge(left=available_user, right=daum_movies, how='left', on='movieId')[['nickName', 'movieId', 'titleKo','rating',  'timestamp', 'numOfSiteRatings']]"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "wG9VkLkZcj7G"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "merged"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "kJkkNuVZcj7G"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "average_ratings = merged.groupby('movieId')['rating'].mean().reset_index()"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "6yP1ggJ1cj7H"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "rating_mean_dict = dict(zip(average_ratings['movieId'], average_ratings['rating']))"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "4vnWCBYPcj7H"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "rating_mean_dict[128434]"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "M5Ha5ipZcj7H"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "from collections import Counter\n",
-    "rating_num_dict = Counter(merged['movieId'])"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "gLCS2SSncj7H"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "rating_num_dict"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "qGMFwaHRcj7H"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "daum_movies['rating_mean'] = daum_movies['movieId'].map(rating_mean_dict)"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "cZjdF5Z9cj7H"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "daum_movies[daum_movies['movieId'] == 128434]"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "mKuDxXm-cj7H"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "daum_movies['num_of_collected_ratings'] = daum_movies['movieId'].map(rating_num_dict)"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "BtjbqH-pcj7H"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "daum_movies"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "YGZjo9hKcj7H"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "daum_ratings[daum_ratings['movieId'] == 128434]['rating'].mean()"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "fw7EZBkycj7H"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "       movieId       titleKo  \\\n",
-       "585       1368        쇼생크 탈출   \n",
-       "18698   111299          더 플랜   \n",
-       "18732   111495        저수지 게임   \n",
-       "13383    75497      또 하나의 약속   \n",
-       "18990   113317          공범자들   \n",
-       "927       1911        터미네이터2   \n",
-       "20172   121059          가버나움   \n",
-       "13468    76325           변호인   \n",
-       "17450   102536            자백   \n",
-       "12108    66814        부러진 화살   \n",
-       "21606   130923           김복동   \n",
-       "476       1173     레옹 디 오리지널   \n",
-       "15898    91662            귀향   \n",
-       "7799     43188           월-E   \n",
-       "19          21     8월의 크리스마스   \n",
-       "18316   108944        노무현입니다   \n",
-       "17492   102840   나, 다니엘 블레이크   \n",
-       "12754    70845        MB의 추억   \n",
-       "18263   108595          1987   \n",
-       "2057      3972           클래식   \n",
-       "19942   119688         그날 바다   \n",
-       "15463    88598          다이빙벨   \n",
-       "6821     41156      브이 포 벤데타   \n",
-       "1963      3752         피아니스트   \n",
-       "20586   123948          그린 북   \n",
-       "18292   108740            원더   \n",
-       "13825    78539      천안함 프로젝트   \n",
-       "2157      4238        살인의 추억   \n",
-       "12848    71486       남영동1985   \n",
-       "17618   103818  무현, 두 도시 이야기   \n",
-       "\n",
-       "                                                 titleEn  \\\n",
-       "585                        The Shawshank Redemption 1994   \n",
-       "18698                                               None   \n",
-       "18732                                               None   \n",
-       "13383                                Another Family 2013   \n",
-       "18990                                               None   \n",
-       "927    Terminator 2 : Judgment Day Terminator 2 - Le ...   \n",
-       "20172                          Capernaum Capharnaüm 2018   \n",
-       "13468                                  The Attorney 2013   \n",
-       "17450                                               None   \n",
-       "12108                                       Unbowed 2011   \n",
-       "21606                                               None   \n",
-       "476                                                 None   \n",
-       "15898                                               None   \n",
-       "7799                                         Wall-E 2008   \n",
-       "19                              Christmas in August 1998   \n",
-       "18316                                               None   \n",
-       "17492                                               None   \n",
-       "12754                             Remembrance of MB 2012   \n",
-       "18263                                               None   \n",
-       "2057                                    The Classic 2002   \n",
-       "19942                                     Intention 2018   \n",
-       "15463                                               None   \n",
-       "6821                                 V for Vendetta 2005   \n",
-       "1963                        The Pianist Le Pianiste 2002   \n",
-       "20586                                               None   \n",
-       "18292                                               None   \n",
-       "13825                                               None   \n",
-       "2157                             Memories of Murder 2003   \n",
-       "12848                             Namyeong-dong1985 2012   \n",
-       "17618                                               None   \n",
-       "\n",
-       "                                             mainPageUrl  \\\n",
-       "585    https://movie.daum.net/moviedb/grade?movieId=1368   \n",
-       "18698  https://movie.daum.net/moviedb/main?movieId=11...   \n",
-       "18732  https://movie.daum.net/moviedb/main?movieId=11...   \n",
-       "13383  https://movie.daum.net/moviedb/grade?movieId=7...   \n",
-       "18990  https://movie.daum.net/moviedb/main?movieId=11...   \n",
-       "927    https://movie.daum.net/moviedb/grade?movieId=1911   \n",
-       "20172  https://movie.daum.net/moviedb/grade?movieId=1...   \n",
-       "13468  https://movie.daum.net/moviedb/grade?movieId=7...   \n",
-       "17450  https://movie.daum.net/moviedb/main?movieId=10...   \n",
-       "12108  https://movie.daum.net/moviedb/grade?movieId=6...   \n",
-       "21606  https://movie.daum.net/moviedb/main?movieId=13...   \n",
-       "476     https://movie.daum.net/moviedb/main?movieId=1173   \n",
-       "15898  https://movie.daum.net/moviedb/main?movieId=91662   \n",
-       "7799   https://movie.daum.net/moviedb/grade?movieId=4...   \n",
-       "19       https://movie.daum.net/moviedb/grade?movieId=21   \n",
-       "18316  https://movie.daum.net/moviedb/main?movieId=10...   \n",
-       "17492  https://movie.daum.net/moviedb/main?movieId=10...   \n",
-       "12754  https://movie.daum.net/moviedb/grade?movieId=7...   \n",
-       "18263  https://movie.daum.net/moviedb/main?movieId=10...   \n",
-       "2057   https://movie.daum.net/moviedb/grade?movieId=3972   \n",
-       "19942  https://movie.daum.net/moviedb/grade?movieId=1...   \n",
-       "15463  https://movie.daum.net/moviedb/main?movieId=88598   \n",
-       "6821   https://movie.daum.net/moviedb/grade?movieId=4...   \n",
-       "1963   https://movie.daum.net/moviedb/grade?movieId=3752   \n",
-       "20586  https://movie.daum.net/moviedb/main?movieId=12...   \n",
-       "18292  https://movie.daum.net/moviedb/main?movieId=10...   \n",
-       "13825  https://movie.daum.net/moviedb/main?movieId=78539   \n",
-       "2157   https://movie.daum.net/moviedb/grade?movieId=4238   \n",
-       "12848  https://movie.daum.net/moviedb/grade?movieId=7...   \n",
-       "17618  https://movie.daum.net/moviedb/main?movieId=10...   \n",
-       "\n",
-       "                                               posterUrl  numOfSiteRatings  \\\n",
-       "585    https://img1.daumcdn.net/thumb/C408x596/?fname...            1613.0   \n",
-       "18698  https://img1.daumcdn.net/thumb/C408x596/?fname...               NaN   \n",
-       "18732  https://img1.daumcdn.net/thumb/C408x596/?fname...               NaN   \n",
-       "13383  https://img1.daumcdn.net/thumb/C408x596/?fname...            4495.0   \n",
-       "18990  https://img1.daumcdn.net/thumb/C408x596/?fname...               NaN   \n",
-       "927    https://img1.daumcdn.net/thumb/C408x596/?fname...             912.0   \n",
-       "20172  https://img1.daumcdn.net/thumb/C408x596/?fname...             553.0   \n",
-       "13468  https://img1.daumcdn.net/thumb/C408x596/?fname...           34631.0   \n",
-       "17450  https://img1.daumcdn.net/thumb/C408x596/?fname...               NaN   \n",
-       "12108  https://img1.daumcdn.net/thumb/C408x596/?fname...            3071.0   \n",
-       "21606  https://img1.daumcdn.net/thumb/C408x596/?fname...               NaN   \n",
-       "476    https://img1.daumcdn.net/thumb/C408x596/?fname...               NaN   \n",
-       "15898  https://img1.daumcdn.net/thumb/C408x596/?fname...               NaN   \n",
-       "7799   https://img1.daumcdn.net/thumb/C408x596/?fname...            1125.0   \n",
-       "19     https://img1.daumcdn.net/thumb/C408x596/?fname...             703.0   \n",
-       "18316  https://img1.daumcdn.net/thumb/C408x596/?fname...               NaN   \n",
-       "17492  https://img1.daumcdn.net/thumb/C408x596/?fname...               NaN   \n",
-       "12754  https://img1.daumcdn.net/thumb/C408x596/?fname...            1490.0   \n",
-       "18263  https://img1.daumcdn.net/thumb/C408x596/?fname...               NaN   \n",
-       "2057   https://img1.daumcdn.net/thumb/C408x596/?fname...            4875.0   \n",
-       "19942  https://img1.daumcdn.net/thumb/C408x596/?fname...            5260.0   \n",
-       "15463  https://img1.daumcdn.net/thumb/C408x596/?fname...               NaN   \n",
-       "6821   https://img1.daumcdn.net/thumb/C408x596/?fname...            1158.0   \n",
-       "1963   https://img1.daumcdn.net/thumb/C408x596/?fname...             727.0   \n",
-       "20586  https://img1.daumcdn.net/thumb/C408x596/?fname...               NaN   \n",
-       "18292  https://img1.daumcdn.net/thumb/C408x596/?fname...               NaN   \n",
-       "13825  https://img1.daumcdn.net/thumb/C408x596/?fname...               NaN   \n",
-       "2157   https://img1.daumcdn.net/thumb/C408x596/?fname...            1757.0   \n",
-       "12848  https://img1.daumcdn.net/thumb/C408x596/?fname...            1811.0   \n",
-       "17618  https://img1.daumcdn.net/thumb/C408x596/?fname...               NaN   \n",
-       "\n",
-       "       rating_mean  num_of_collected_ratings  \n",
-       "585       9.758427                       178  \n",
-       "18698     9.693487                       261  \n",
-       "18732     9.657143                       140  \n",
-       "13383     9.645985                       274  \n",
-       "18990     9.623810                       210  \n",
-       "927       9.593220                       118  \n",
-       "20172     9.582192                       146  \n",
-       "13468     9.580556                      1440  \n",
-       "17450     9.572222                       180  \n",
-       "12108     9.521739                       161  \n",
-       "21606     9.518868                       106  \n",
-       "476       9.508621                       116  \n",
-       "15898     9.503797                       395  \n",
-       "7799      9.500000                       104  \n",
-       "19        9.471074                       121  \n",
-       "18316     9.459524                       420  \n",
-       "17492     9.446429                       112  \n",
-       "12754     9.441176                       102  \n",
-       "18263     9.420997                       943  \n",
-       "2057      9.380435                       184  \n",
-       "19942     9.370000                       400  \n",
-       "15463     9.330218                       321  \n",
-       "6821      9.312977                       131  \n",
-       "1963      9.290909                       110  \n",
-       "20586     9.289683                       252  \n",
-       "18292     9.279279                       111  \n",
-       "13825     9.275109                       229  \n",
-       "2157      9.271357                       199  \n",
-       "12848     9.266667                       150  \n",
-       "17618     9.260204                       196  "
-      ],
-      "text/html": [
-       "\n",
-       "  <div id=\"df-231230a7-1840-4772-b990-c227d84e8df9\" class=\"colab-df-container\">\n",
-       "    <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>movieId</th>\n",
-       "      <th>titleKo</th>\n",
-       "      <th>titleEn</th>\n",
-       "      <th>mainPageUrl</th>\n",
-       "      <th>posterUrl</th>\n",
-       "      <th>numOfSiteRatings</th>\n",
-       "      <th>rating_mean</th>\n",
-       "      <th>num_of_collected_ratings</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>585</th>\n",
-       "      <td>1368</td>\n",
-       "      <td>쇼생크 탈출</td>\n",
-       "      <td>The Shawshank Redemption 1994</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=1368</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>1613.0</td>\n",
-       "      <td>9.758427</td>\n",
-       "      <td>178</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18698</th>\n",
-       "      <td>111299</td>\n",
-       "      <td>더 플랜</td>\n",
-       "      <td>None</td>\n",
-       "      <td>https://movie.daum.net/moviedb/main?movieId=11...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.693487</td>\n",
-       "      <td>261</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18732</th>\n",
-       "      <td>111495</td>\n",
-       "      <td>저수지 게임</td>\n",
-       "      <td>None</td>\n",
-       "      <td>https://movie.daum.net/moviedb/main?movieId=11...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.657143</td>\n",
-       "      <td>140</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13383</th>\n",
-       "      <td>75497</td>\n",
-       "      <td>또 하나의 약속</td>\n",
-       "      <td>Another Family 2013</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=7...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>4495.0</td>\n",
-       "      <td>9.645985</td>\n",
-       "      <td>274</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18990</th>\n",
-       "      <td>113317</td>\n",
-       "      <td>공범자들</td>\n",
-       "      <td>None</td>\n",
-       "      <td>https://movie.daum.net/moviedb/main?movieId=11...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.623810</td>\n",
-       "      <td>210</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>927</th>\n",
-       "      <td>1911</td>\n",
-       "      <td>터미네이터2</td>\n",
-       "      <td>Terminator 2 : Judgment Day Terminator 2 - Le ...</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=1911</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>912.0</td>\n",
-       "      <td>9.593220</td>\n",
-       "      <td>118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20172</th>\n",
-       "      <td>121059</td>\n",
-       "      <td>가버나움</td>\n",
-       "      <td>Capernaum Capharnaüm 2018</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=1...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>553.0</td>\n",
-       "      <td>9.582192</td>\n",
-       "      <td>146</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13468</th>\n",
-       "      <td>76325</td>\n",
-       "      <td>변호인</td>\n",
-       "      <td>The Attorney 2013</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=7...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>34631.0</td>\n",
-       "      <td>9.580556</td>\n",
-       "      <td>1440</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17450</th>\n",
-       "      <td>102536</td>\n",
-       "      <td>자백</td>\n",
-       "      <td>None</td>\n",
-       "      <td>https://movie.daum.net/moviedb/main?movieId=10...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.572222</td>\n",
-       "      <td>180</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12108</th>\n",
-       "      <td>66814</td>\n",
-       "      <td>부러진 화살</td>\n",
-       "      <td>Unbowed 2011</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=6...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>3071.0</td>\n",
-       "      <td>9.521739</td>\n",
-       "      <td>161</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21606</th>\n",
-       "      <td>130923</td>\n",
-       "      <td>김복동</td>\n",
-       "      <td>None</td>\n",
-       "      <td>https://movie.daum.net/moviedb/main?movieId=13...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.518868</td>\n",
-       "      <td>106</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>476</th>\n",
-       "      <td>1173</td>\n",
-       "      <td>레옹 디 오리지널</td>\n",
-       "      <td>None</td>\n",
-       "      <td>https://movie.daum.net/moviedb/main?movieId=1173</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.508621</td>\n",
-       "      <td>116</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15898</th>\n",
-       "      <td>91662</td>\n",
-       "      <td>귀향</td>\n",
-       "      <td>None</td>\n",
-       "      <td>https://movie.daum.net/moviedb/main?movieId=91662</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.503797</td>\n",
-       "      <td>395</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7799</th>\n",
-       "      <td>43188</td>\n",
-       "      <td>월-E</td>\n",
-       "      <td>Wall-E 2008</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=4...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>1125.0</td>\n",
-       "      <td>9.500000</td>\n",
-       "      <td>104</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>21</td>\n",
-       "      <td>8월의 크리스마스</td>\n",
-       "      <td>Christmas in August 1998</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=21</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>703.0</td>\n",
-       "      <td>9.471074</td>\n",
-       "      <td>121</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18316</th>\n",
-       "      <td>108944</td>\n",
-       "      <td>노무현입니다</td>\n",
-       "      <td>None</td>\n",
-       "      <td>https://movie.daum.net/moviedb/main?movieId=10...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.459524</td>\n",
-       "      <td>420</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17492</th>\n",
-       "      <td>102840</td>\n",
-       "      <td>나, 다니엘 블레이크</td>\n",
-       "      <td>None</td>\n",
-       "      <td>https://movie.daum.net/moviedb/main?movieId=10...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.446429</td>\n",
-       "      <td>112</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12754</th>\n",
-       "      <td>70845</td>\n",
-       "      <td>MB의 추억</td>\n",
-       "      <td>Remembrance of MB 2012</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=7...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>1490.0</td>\n",
-       "      <td>9.441176</td>\n",
-       "      <td>102</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18263</th>\n",
-       "      <td>108595</td>\n",
-       "      <td>1987</td>\n",
-       "      <td>None</td>\n",
-       "      <td>https://movie.daum.net/moviedb/main?movieId=10...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.420997</td>\n",
-       "      <td>943</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2057</th>\n",
-       "      <td>3972</td>\n",
-       "      <td>클래식</td>\n",
-       "      <td>The Classic 2002</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=3972</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>4875.0</td>\n",
-       "      <td>9.380435</td>\n",
-       "      <td>184</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19942</th>\n",
-       "      <td>119688</td>\n",
-       "      <td>그날 바다</td>\n",
-       "      <td>Intention 2018</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=1...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>5260.0</td>\n",
-       "      <td>9.370000</td>\n",
-       "      <td>400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15463</th>\n",
-       "      <td>88598</td>\n",
-       "      <td>다이빙벨</td>\n",
-       "      <td>None</td>\n",
-       "      <td>https://movie.daum.net/moviedb/main?movieId=88598</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.330218</td>\n",
-       "      <td>321</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6821</th>\n",
-       "      <td>41156</td>\n",
-       "      <td>브이 포 벤데타</td>\n",
-       "      <td>V for Vendetta 2005</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=4...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>1158.0</td>\n",
-       "      <td>9.312977</td>\n",
-       "      <td>131</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1963</th>\n",
-       "      <td>3752</td>\n",
-       "      <td>피아니스트</td>\n",
-       "      <td>The Pianist Le Pianiste 2002</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=3752</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>727.0</td>\n",
-       "      <td>9.290909</td>\n",
-       "      <td>110</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20586</th>\n",
-       "      <td>123948</td>\n",
-       "      <td>그린 북</td>\n",
-       "      <td>None</td>\n",
-       "      <td>https://movie.daum.net/moviedb/main?movieId=12...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.289683</td>\n",
-       "      <td>252</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18292</th>\n",
-       "      <td>108740</td>\n",
-       "      <td>원더</td>\n",
-       "      <td>None</td>\n",
-       "      <td>https://movie.daum.net/moviedb/main?movieId=10...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.279279</td>\n",
-       "      <td>111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13825</th>\n",
-       "      <td>78539</td>\n",
-       "      <td>천안함 프로젝트</td>\n",
-       "      <td>None</td>\n",
-       "      <td>https://movie.daum.net/moviedb/main?movieId=78539</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.275109</td>\n",
-       "      <td>229</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2157</th>\n",
-       "      <td>4238</td>\n",
-       "      <td>살인의 추억</td>\n",
-       "      <td>Memories of Murder 2003</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=4238</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>1757.0</td>\n",
-       "      <td>9.271357</td>\n",
-       "      <td>199</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12848</th>\n",
-       "      <td>71486</td>\n",
-       "      <td>남영동1985</td>\n",
-       "      <td>Namyeong-dong1985 2012</td>\n",
-       "      <td>https://movie.daum.net/moviedb/grade?movieId=7...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>1811.0</td>\n",
-       "      <td>9.266667</td>\n",
-       "      <td>150</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17618</th>\n",
-       "      <td>103818</td>\n",
-       "      <td>무현, 두 도시 이야기</td>\n",
-       "      <td>None</td>\n",
-       "      <td>https://movie.daum.net/moviedb/main?movieId=10...</td>\n",
-       "      <td>https://img1.daumcdn.net/thumb/C408x596/?fname...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.260204</td>\n",
-       "      <td>196</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-231230a7-1840-4772-b990-c227d84e8df9')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-231230a7-1840-4772-b990-c227d84e8df9 button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-231230a7-1840-4772-b990-c227d84e8df9');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "<div id=\"df-3f030a2e-1fa7-4d72-9aaf-47fa66706407\">\n",
-       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3f030a2e-1fa7-4d72-9aaf-47fa66706407')\"\n",
-       "            title=\"Suggest charts\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "     width=\"24px\">\n",
-       "    <g>\n",
-       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-       "    </g>\n",
-       "</svg>\n",
-       "  </button>\n",
-       "\n",
-       "<style>\n",
-       "  .colab-df-quickchart {\n",
-       "      --bg-color: #E8F0FE;\n",
-       "      --fill-color: #1967D2;\n",
-       "      --hover-bg-color: #E2EBFA;\n",
-       "      --hover-fill-color: #174EA6;\n",
-       "      --disabled-fill-color: #AAA;\n",
-       "      --disabled-bg-color: #DDD;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart {\n",
-       "      --bg-color: #3B4455;\n",
-       "      --fill-color: #D2E3FC;\n",
-       "      --hover-bg-color: #434B5C;\n",
-       "      --hover-fill-color: #FFFFFF;\n",
-       "      --disabled-bg-color: #3B4455;\n",
-       "      --disabled-fill-color: #666;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart {\n",
-       "    background-color: var(--bg-color);\n",
-       "    border: none;\n",
-       "    border-radius: 50%;\n",
-       "    cursor: pointer;\n",
-       "    display: none;\n",
-       "    fill: var(--fill-color);\n",
-       "    height: 32px;\n",
-       "    padding: 0;\n",
-       "    width: 32px;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart:hover {\n",
-       "    background-color: var(--hover-bg-color);\n",
-       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "    fill: var(--button-hover-fill-color);\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart-complete:disabled,\n",
-       "  .colab-df-quickchart-complete:disabled:hover {\n",
-       "    background-color: var(--disabled-bg-color);\n",
-       "    fill: var(--disabled-fill-color);\n",
-       "    box-shadow: none;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-spinner {\n",
-       "    border: 2px solid var(--fill-color);\n",
-       "    border-color: transparent;\n",
-       "    border-bottom-color: var(--fill-color);\n",
-       "    animation:\n",
-       "      spin 1s steps(1) infinite;\n",
-       "  }\n",
-       "\n",
-       "  @keyframes spin {\n",
-       "    0% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "      border-left-color: var(--fill-color);\n",
-       "    }\n",
-       "    20% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    30% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    40% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    60% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    80% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "    90% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "  }\n",
-       "</style>\n",
-       "\n",
-       "  <script>\n",
-       "    async function quickchart(key) {\n",
-       "      const quickchartButtonEl =\n",
-       "        document.querySelector('#' + key + ' button');\n",
-       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-       "      try {\n",
-       "        const charts = await google.colab.kernel.invokeFunction(\n",
-       "            'suggestCharts', [key], {});\n",
-       "      } catch (error) {\n",
-       "        console.error('Error during call to suggestCharts:', error);\n",
-       "      }\n",
-       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-       "    }\n",
-       "    (() => {\n",
-       "      let quickchartButtonEl =\n",
-       "        document.querySelector('#df-3f030a2e-1fa7-4d72-9aaf-47fa66706407 button');\n",
-       "      quickchartButtonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "    })();\n",
-       "  </script>\n",
-       "</div>\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 72
-    }
-   ],
-   "source": [
-    "daum_movies[daum_movies['num_of_collected_ratings']>100].sort_values('rating_mean', ascending=False).head(30)"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "-pwiAudTcj7H",
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
-    },
-    "executionInfo": {
-     "status": "ok",
-     "timestamp": 1703382059698,
-     "user_tz": -540,
-     "elapsed": 1464,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     }
-    },
-    "outputId": "59682c8e-5df9-4898-d55b-421467208b89"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[(None, 1441),\n",
-       " ('변호인', 1440),\n",
-       " ('기생충', 1233),\n",
-       " ('1987', 943),\n",
-       " ('반도', 797),\n",
-       " ('극한직업', 793),\n",
-       " ('백두산', 785),\n",
-       " ('남산의 부장들', 741),\n",
-       " ('강철비2: 정상회담', 728),\n",
-       " ('신과함께-죄와 벌', 726),\n",
-       " ('택시운전사', 711),\n",
-       " ('봉오동 전투', 689),\n",
-       " ('군함도', 676),\n",
-       " ('조커', 675),\n",
-       " ('82년생 김지영', 670),\n",
-       " ('헌트', 669),\n",
-       " ('승리호', 656),\n",
-       " ('명량', 653),\n",
-       " ('안시성', 634),\n",
-       " ('서울의 봄', 630),\n",
-       " ('부산행', 625),\n",
-       " ('인터스텔라', 613),\n",
-       " ('엑시트', 609),\n",
-       " ('어벤져스: 엔드게임', 603),\n",
-       " ('보헤미안 랩소디', 585),\n",
-       " ('곡성', 584),\n",
-       " ('다만 악에서 구하소서', 582),\n",
-       " ('암살', 560),\n",
-       " ('마녀', 555),\n",
-       " ('국제시장', 530),\n",
-       " ('강철비', 527),\n",
-       " ('탑건: 매버릭', 525),\n",
-       " ('공작', 523),\n",
-       " ('신과함께-인과 연', 520),\n",
-       " ('설국열차', 509),\n",
-       " ('한산: 용의 출현', 506),\n",
-       " ('#살아있다', 499),\n",
-       " ('비상선언', 493),\n",
-       " ('외계+인 1부', 490),\n",
-       " ('26년', 490),\n",
-       " ('캡틴 마블', 480),\n",
-       " ('범죄도시2', 479),\n",
-       " ('아수라', 470),\n",
-       " ('인천상륙작전', 458),\n",
-       " ('헤어질 결심', 456),\n",
-       " ('범죄도시3', 454),\n",
-       " ('베테랑', 447),\n",
-       " ('모가디슈', 441),\n",
-       " ('내부자들', 436),\n",
-       " ('낙원의 밤', 435),\n",
-       " ('더 킹', 434),\n",
-       " ('나랏말싸미', 432),\n",
-       " ('연평해전', 423),\n",
-       " ('노무현입니다', 420),\n",
-       " ('불한당: 나쁜 놈들의 세상', 419),\n",
-       " ('사바하', 417),\n",
-       " ('히트맨', 415),\n",
-       " ('귀향', 415),\n",
-       " ('길복순', 412),\n",
-       " ('범죄도시', 409),\n",
-       " ('인랑', 408),\n",
-       " ('증인', 403),\n",
-       " ('알라딘', 402),\n",
-       " ('터미네이터: 다크 페이트', 402),\n",
-       " ('그날 바다', 400),\n",
-       " ('마녀(魔女) Part2. The Other One', 398),\n",
-       " ('아바타: 물의 길', 392),\n",
-       " ('걸캅스', 389),\n",
-       " ('밀정', 384),\n",
-       " ('매드 맥스: 분노의 도로', 381),\n",
-       " ('독전', 379),\n",
-       " ('뮬란', 377),\n",
-       " ('군도:민란의 시대', 376),\n",
-       " ('어벤져스: 인피니티 워', 376),\n",
-       " ('아가씨', 375),\n",
-       " ('국가부도의 날', 373),\n",
-       " ('나를 찾아줘', 370),\n",
-       " ('영웅', 365),\n",
-       " ('악인전', 364),\n",
-       " ('블랙머니', 364),\n",
-       " ('겨울왕국 2', 364),\n",
-       " ('밀수', 358),\n",
-       " ('지푸라기라도 잡고 싶은 짐승들', 358),\n",
-       " ('콘크리트 유토피아', 355),\n",
-       " ('카터', 354),\n",
-       " ('완벽한 타인', 354),\n",
-       " ('테넷', 353),\n",
-       " ('남한산성', 352),\n",
-       " ('말모이', 351),\n",
-       " ('그것만이 내 세상', 350),\n",
-       " ('사냥의 시간', 348),\n",
-       " ('아저씨', 347),\n",
-       " ('아쿠아맨', 344),\n",
-       " ('천문: 하늘에 묻는다', 343),\n",
-       " ('시동', 341),\n",
-       " ('타짜: 원 아이드 잭', 337),\n",
-       " ('이터널스', 334),\n",
-       " ('조작된 도시', 328),\n",
-       " ('사도', 328),\n",
-       " ('버닝', 325)]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 73
-    }
-   ],
-   "source": [
-    "Counter(merged['titleKo']).most_common(100)"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "v0ZgUyNecj7H",
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "status": "ok",
-     "timestamp": 1703382065832,
-     "user_tz": -540,
-     "elapsed": 520,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     }
-    },
-    "outputId": "e7703bea-3b79-4955-d37e-c4bcdf0f1536"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "# # movie_dictionary\n",
-    "# movies = pd.read_table('../data/ml-1m_grouplens/movies.dat', sep='::', header=None, names=['movie_id', 'title', 'genres'],\n",
-    "#                        engine='python', encoding_errors='ignore')\n",
-    "# movie_dict = movies.to_dict('index')"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "dH2kotujcj7I"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "# # movie_dictionary\n",
-    "# movies = pd.read_table('../data/ml-1m_grouplens/movies.dat', sep='::', header=None, names=['movie_id', 'title', 'genres'],\n",
-    "#                        engine='python', encoding_errors='ignore')\n",
-    "# movie_dict = movies.to_dict('index')"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "quOskqH_cj7I"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "# ratings = pd.read_table('../data/ml-1m_grouplens/ratings.dat', sep='::', header=None, names=['userid', 'movieid', 'rating', 'timestamp'],\n",
-    "#                        engine='python', encoding_errors='ignore')"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "uY2MvbFXcj7I"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "merged.nunique()"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "KRdqOZ4Vcj7I"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "user_id_dict = {nn:i for i, nn in enumerate(merged['nickName'].unique())}"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "sm06Rn9acj7I"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "movie_id_dict = {site_mid:i for i, site_mid in enumerate(merged['movieId'].unique())}"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "KTQpxXAxcj7I"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "merged['uid'] = merged['nickName'].map(user_id_dict)\n",
-    "merged['iid'] = merged['movieId'].map(movie_id_dict)\n",
-    "merged"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "NvblNmERcj7I"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "from datetime import datetime"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "lKtgdOmVcj7I"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "merged['dt'] = merged['timestamp'].map(lambda x: datetime.fromtimestamp(x))"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "1PxKRQiDcj7I"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "merged"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "9RWQRR64cj7I"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "sorted_iid_lists = merged.sort_values(by=['uid', 'timestamp']).groupby('uid')['iid'].apply(list)\n",
-    "sorted_iid_lists"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "Aak5Mi61cj7I"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "sorted_iid_dict = dict(sorted_iid_lists)"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "W34KxAQBcj7I"
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# 모델 준비"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    },
-    "id": "HcqdE8eqcj7J"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "import torch\n",
-    "from torch import nn\n",
-    "import argparse\n",
-    "from model import SASRec\n",
-    "from utils import *"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "3yXU5MQScj7K"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "def str2bool(s):\n",
-    "    if s not in {'false', 'true'}:\n",
-    "        raise ValueError('Not a valid boolean string')\n",
-    "    return s == 'true'\n",
-    "\n",
-    "def get_args():\n",
-    "    parser = argparse.ArgumentParser()\n",
-    "    parser.add_argument('--ratings_dir', required=True)\n",
-    "    parser.add_argument('--model_output_dir', required=True)\n",
-    "    parser.add_argument('--batch_size', default=128, type=int)\n",
-    "    parser.add_argument('--lr', default=0.001, type=float)\n",
-    "    parser.add_argument('--maxlen', default=50, type=int)\n",
-    "    parser.add_argument('--hidden_units', default=50, type=int)\n",
-    "    parser.add_argument('--num_blocks', default=2, type=int)\n",
-    "    parser.add_argument('--num_epochs', default=200, type=int)\n",
-    "    parser.add_argument('--num_heads', default=1, type=int)\n",
-    "    parser.add_argument('--dropout_rate', default=0.5, type=float)\n",
-    "    parser.add_argument('--l2_emb', default=0.0, type=float)\n",
-    "    parser.add_argument('--device', default='cpu', type=str)\n",
-    "    parser.add_argument('--inference_only', default=False, type=str2bool)\n",
-    "    parser.add_argument('--state_dict_path', default=None, type=str)\n",
-    "    return parser.parse_args(args=['--ratings_dir', '../data/ml-1m_grouplens/ratings.dat', '--model_output_dir', 'model_output'])\n",
-    "args = get_args()"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "dPvBk_yfcj7K"
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# 학습모델 저장 경로"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    },
-    "id": "wETEVDG_cj7K"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "print(f\"args.model_output_dir : {args.model_output_dir}\")\n",
-    "if not os.path.exists(args.model_output_dir):\n",
-    "    os.mkdir(args.model_output_dir)"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "oaC-scxWcj7K"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "# [str(k) + ',' + str(v)\n",
-    "for k, v in sorted(vars(args).items(), key=lambda x: x[0]):\n",
-    "    print(f\"{k:30} : {str(v):20}\")"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "s8L3ScvGcj7K"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "args.dataset = 'daum'"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "QKkdzEOQcj7K"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "args.train_dir = 'train_dir'"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "ObpkC9hjcj7K"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "if not os.path.isdir(args.dataset + '_' + args.train_dir):  # 데이터 없다면 폴더 만듦\n",
-    "    os.makedirs(args.dataset + '_' + args.train_dir)\n",
-    "    print(f\"made {args.dataset + '_' + args.train_dir} folder\")\n",
-    "with open(os.path.join(args.dataset + '_' + args.train_dir, 'args.txt'), 'w') as f:  # argument 저장\n",
-    "    f.write('\\n'.join([str(k) + ',' + str(v) for k, v in sorted(vars(args).items(), key=lambda x: x[0])]))\n",
-    "    print(f\"wrote '{os.path.join(args.dataset + '_' + args.train_dir, 'args.txt')}'\")\n",
-    "f.close()"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "s0jx1kp2cj7K"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "args.ratings_dir"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "P-I8I_WDcj7K"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "from collections import defaultdict"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "TC3EGiGPcj7K"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "f = open(args.ratings_dir, 'r')\n",
-    "user_seqs = defaultdict(list)\n",
-    "for line in f.readlines():\n",
-    "    data_lst = line.replace('\\n', '').split('::')\n",
-    "    if len(data_lst) < 4:\n",
-    "        continue\n",
-    "    userid, movieid, rating, timestamp = [int(data) for data in line.replace('\\n', '').split('::')]\n",
-    "    user_seqs[userid].append((movieid, timestamp))\n",
-    "\n",
-    "f.close()"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "B-vj4qrrcj7K"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "min(user_seqs.keys())"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "5cwBpZsHcj7L"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "max(user_seqs.keys())"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "zAfwMnI-cj7L"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "\n",
-    "len(user_seqs.keys())"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "JnLb_c5Pcj7L"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "for userid in list(user_seqs.keys()):\n",
-    "    sorted_user_seq = sorted(user_seqs[userid], key=lambda x: x[1])\n",
-    "    sorted_user_seq = [movieid for movieid, timestamp in sorted_user_seq]\n",
-    "    user_seqs[userid] = sorted_user_seq"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "w8WBj-y5cj7L"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "from collections import defaultdict\n",
-    "\n",
-    "# train/val/test data generation\n",
-    "def data_partition(fname):\n",
-    "    usernum = 0\n",
-    "    itemnum = 0\n",
-    "    User = defaultdict(list)\n",
-    "    user_train = {}\n",
-    "    user_valid = {}\n",
-    "    user_test = {}\n",
-    "    # assume user/item index starting from 1\n",
-    "    f = open(f'{fname}')\n",
-    "    for line in f:\n",
-    "        u, i = line.rstrip().split(' ')\n",
-    "        u = int(u)\n",
-    "        i = int(i)\n",
-    "        usernum = max(u, usernum)\n",
-    "        itemnum = max(i, itemnum)\n",
-    "        User[u].append(i)\n",
-    "\n",
-    "    for user in User:\n",
-    "        nfeedback = len(User[user])\n",
-    "        if nfeedback < 3:\n",
-    "            user_train[user] = User[user]\n",
-    "            user_valid[user] = []\n",
-    "            user_test[user] = []\n",
-    "        else:\n",
-    "            user_train[user] = User[user][:-2]\n",
-    "            user_valid[user] = []\n",
-    "            user_valid[user].append(User[user][-2])\n",
-    "            user_test[user] = []\n",
-    "            user_test[user].append(User[user][-1])\n",
-    "    return [user_train, user_valid, user_test, usernum, itemnum]\n",
-    "\n",
-    "dataset = data_partition(args.dataset)"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "F3dQNoQIcj7L"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "user_train = {}\n",
-    "user_valid = {}\n",
-    "user_test = {}\n",
-    "\n",
-    "for uid, seq in sorted_iid_dict.items():\n",
-    "    nfeedback = len(seq)\n",
-    "    if nfeedback < 3:\n",
-    "        user_train[uid] = seq\n",
-    "        user_valid[uid] = []\n",
-    "        user_test[uid] = []\n",
-    "    else:\n",
-    "        user_train[uid] = seq[:-2]\n",
-    "        user_valid[uid] = []\n",
-    "        user_valid[uid].append(seq[-2])\n",
-    "        user_test[uid] = []\n",
-    "        user_test[uid].append(seq[-1])\n",
-    "\n",
-    "usernum = merged['uid'].nunique()\n",
-    "itemnum = merged['iid'].nunique()\n",
-    "dataset = [user_train, user_valid, user_test, usernum, itemnum]"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "0hv7KVP3cj7M"
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "![image.png](data:image/png;base64,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)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    },
-    "id": "1X1cXEHCcj7M"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "print(f\"유저 최소값 : {min(user_train.keys())}\")\n",
-    "print(f\"유저 최대값 : {max(user_train.keys()):,}\")\n",
-    "print(f\"유저 수 : {len(user_train):,}\")"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "Nc5Lt6u5cj7T"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "num_batch = len(user_train) // args.batch_size # tail? + ((len(user_train) % args.batch_size) != 0)\n",
-    "cc = 0.0\n",
-    "for u in user_train:\n",
-    "    cc += len(user_train[u])\n",
-    "\n",
-    "print(f\"len(user_train) : {len(user_train)}\")\n",
-    "print(f\"args.batch_size : {args.batch_size}\")\n",
-    "print(f\"num_batch : {num_batch}\")\n",
-    "print(f\"average sequence length : {(cc / len(user_train)):.2f}\")"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "SO20NHdWcj7T"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "f = open(os.path.join(args.dataset + '_' + args.train_dir, 'log.txt'), 'w')\n",
-    "print(f\"logging 위치 : {os.path.join(args.dataset + '_' + args.train_dir, 'log.txt')}\")"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "Bo67TKhdcj7T"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "sampler = WarpSampler(user_train, usernum, itemnum, batch_size=args.batch_size, maxlen=args.maxlen, n_workers=3)\n",
-    "model = SASRec(usernum, itemnum, args).to(args.device) # no ReLU activation in original SASRec implementation?"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "kWBc0AS7cj7T"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "# model = SASRec(10, 100, args).to(args.device) # no ReLU activation in original SASRec implementation?"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "8_VWit6jcj7T"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "epoch_start_idx = 1\n",
-    "if args.state_dict_path is not None:\n",
-    "    try:\n",
-    "        model.load_state_dict(torch.load(args.state_dict_path, map_location=torch.device(args.device)))\n",
-    "        tail = args.state_dict_path[args.state_dict_path.find('epoch=') + 6:]\n",
-    "        epoch_start_idx = int(tail[:tail.find('.')]) + 1\n",
-    "    except: # in case your pytorch version is not 1.6 etc., pls debug by pdb if load weights failed\n",
-    "        print('failed loading state_dicts, pls check file path: ', end=\"\")\n",
-    "        print(args.state_dict_path)\n",
-    "        print('pdb enabled for your quick check, pls type exit() if you do not need it')\n",
-    "        import pdb; pdb.set_trace()"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "VcbpaC3scj7T"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "if args.inference_only:\n",
-    "    model.eval()\n",
-    "    t_test = evaluate(model, dataset, args)\n",
-    "    print('test (NDCG@10: %.4f, HR@10: %.4f)' % (t_test[0], t_test[1]))"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "d-bNgUWscj7U"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "# ce_criterion = torch.nn.CrossEntropyLoss()\n",
-    "# https://github.com/NVIDIA/pix2pixHD/issues/9 how could an old bug appear again...\n",
-    "bce_criterion = torch.nn.BCEWithLogitsLoss() # torch.nn.BCELoss()\n",
-    "adam_optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, betas=(0.9, 0.98))\n",
-    "\n",
-    "T = 0.0\n",
-    "t0 = time.time()"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "-V2TDjcycj7U"
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# Wandb 설정"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    },
-    "id": "1KYQr-0ccj7U"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "pip install wandb"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "v1lHYaLdcj7W"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "import wandb"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "lUz--E1lcj7W"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "wandb.login()"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "ximTBLEfcj7W"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "model_name = 'SASRec'"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "eXXlkKrPcj7W"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "# 🐝 1️⃣ Start a new run to track this script\n",
-    "wandb.init(\n",
-    "    # Set the project where this run will be logged\n",
-    "    project=\"recsys_key_papers_implementation\",\n",
-    "    # We pass a run name (otherwise it’ll be randomly assigned, like sunshine-lollypop-10)\n",
-    "    name=f\"{model_name}\",\n",
-    "    # Track hyperparameters and run metadata\n",
-    "    config=vars(args))"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "jmUSyeckcj7W"
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# 학습"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    },
-    "id": "Acjs3F3ocj7W"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "import copy\n",
-    "import random\n",
-    "import numpy as np"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "9OMLh3M2cj7W"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "def evaluate(model, dataset, args):\n",
-    "    [train, valid, test, usernum, itemnum] = copy.deepcopy(dataset)\n",
-    "\n",
-    "    NDCG = 0.0\n",
-    "    HT = 0.0\n",
-    "    valid_user = 0.0\n",
-    "\n",
-    "    if usernum > 10000:\n",
-    "        users = random.sample(range(usernum), 10000)\n",
-    "    else:\n",
-    "        users = range(usernum)\n",
-    "    for u in users:\n",
-    "\n",
-    "        if len(train[u]) < 1 or len(test[u]) < 1: continue\n",
-    "\n",
-    "        seq = np.zeros([args.maxlen], dtype=np.int32)\n",
-    "        idx = args.maxlen - 1\n",
-    "        seq[idx] = valid[u][0]\n",
-    "        idx -= 1\n",
-    "        for i in reversed(train[u]):\n",
-    "            seq[idx] = i\n",
-    "            idx -= 1\n",
-    "            if idx == -1: break\n",
-    "        rated = set(train[u])\n",
-    "        rated.add(0)\n",
-    "        item_idx = [test[u][0]]\n",
-    "        for _ in range(100):\n",
-    "            t = np.random.randint(1, itemnum + 1)\n",
-    "            while t in rated: t = np.random.randint(1, itemnum + 1)\n",
-    "            item_idx.append(t)\n",
-    "\n",
-    "        try:\n",
-    "            predictions = -model.predict(*[np.array(l) for l in [[seq], item_idx]])\n",
-    "        except TypeError as e:\n",
-    "            print([np.array(l) for l in [[u], [seq], item_idx]])\n",
-    "            raise(e)\n",
-    "        predictions = predictions[0]  # - for 1st argsort DESC\n",
-    "\n",
-    "        rank = predictions.argsort().argsort()[0].item()\n",
-    "\n",
-    "        valid_user += 1\n",
-    "\n",
-    "        if rank < 10:\n",
-    "            NDCG += 1 / np.log2(rank + 2)\n",
-    "            HT += 1\n",
-    "        if valid_user % 1000 == 0:\n",
-    "            print('.', end=\"\")\n",
-    "            sys.stdout.flush()\n",
-    "\n",
-    "    return NDCG / valid_user, HT / valid_user"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "QtHS_JNTcj7W"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "def evaluate_valid(model, dataset, args):\n",
-    "    [train, valid, test, usernum, itemnum] = copy.deepcopy(dataset)\n",
-    "\n",
-    "    NDCG = 0.0\n",
-    "    valid_user = 0.0\n",
-    "    HT = 0.0\n",
-    "    if usernum > 10000:\n",
-    "        users = random.sample(range(usernum), 10000)\n",
-    "    else:\n",
-    "        users = range(usernum)\n",
-    "    for u in users:\n",
-    "        if len(train[u]) < 1 or len(valid[u]) < 1: continue\n",
-    "\n",
-    "        seq = np.zeros([args.maxlen], dtype=np.int32)\n",
-    "        idx = args.maxlen - 1\n",
-    "        for i in reversed(train[u]):\n",
-    "            seq[idx] = i\n",
-    "            idx -= 1\n",
-    "            if idx == -1: break\n",
-    "\n",
-    "        rated = set(train[u])\n",
-    "        rated.add(0)\n",
-    "        item_idx = [valid[u][0]]\n",
-    "        for _ in range(100):\n",
-    "            t = np.random.randint(1, itemnum + 1)\n",
-    "            while t in rated: t = np.random.randint(1, itemnum + 1)\n",
-    "            item_idx.append(t)\n",
-    "\n",
-    "        predictions = -model.predict(*[np.array(l) for l in [[seq], item_idx]])\n",
-    "        predictions = predictions[0]\n",
-    "\n",
-    "        rank = predictions.argsort().argsort()[0].item()\n",
-    "\n",
-    "        valid_user += 1\n",
-    "\n",
-    "        if rank < 10:\n",
-    "            NDCG += 1 / np.log2(rank + 2)\n",
-    "            HT += 1\n",
-    "        if valid_user % 100 == 0:\n",
-    "            print('.', end=\"\")\n",
-    "            sys.stdout.flush()\n",
-    "\n",
-    "    return NDCG / valid_user, HT / valid_user"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "qc-QrLRAcj7X"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "best_ndcg = 0\n",
-    "for epoch in range(epoch_start_idx, args.num_epochs + 1):\n",
-    "    if args.inference_only: break # just to decrease identition\n",
-    "    for step in range(num_batch): # tqdm(range(num_batch), total=num_batch, ncols=70, leave=False, unit='b'):\n",
-    "        u, seq, pos, neg = sampler.next_batch() # tuples to ndarray\n",
-    "        # dataset, dataloader\n",
-    "        u, seq, pos, neg = np.array(u), np.array(seq), np.array(pos), np.array(neg)\n",
-    "        pos_logits, neg_logits = model(u, seq, pos, neg)\n",
-    "        pos_labels, neg_labels = torch.ones(pos_logits.shape, device=args.device), torch.zeros(neg_logits.shape, device=args.device)\n",
-    "        # print(\"\\neye ball check raw_logits:\"); print(pos_logits); print(neg_logits) # check pos_logits > 0, neg_logits < 0\n",
-    "        adam_optimizer.zero_grad()\n",
-    "        indices = np.where(pos != 0)\n",
-    "        loss = bce_criterion(pos_logits[indices], pos_labels[indices])\n",
-    "        loss += bce_criterion(neg_logits[indices], neg_labels[indices])\n",
-    "        for param in model.item_emb.parameters(): loss += args.l2_emb * torch.norm(param)\n",
-    "        loss.backward()\n",
-    "        adam_optimizer.step()\n",
-    "    # print(\"loss in epoch {} iteration {}: {}\".format(epoch, step, loss.item())) # expected 0.4~0.6 after init few epochs\n",
-    "\n",
-    "\n",
-    "    # if epoch % 20 == 0:\n",
-    "    model.eval()\n",
-    "    t1 = time.time() - t0\n",
-    "    T += t1\n",
-    "    print('Evaluating', end='')\n",
-    "    t_test = evaluate(model, dataset, args)\n",
-    "    t_valid = evaluate_valid(model, dataset, args)\n",
-    "    print(f\"epoch:{epoch:4}, time: {T//3600:.0f} h {T//60:.0f} min {T%60:.0f} sec, valid (NDCG@10: {t_valid[0]:.4f}, HR@10: {t_valid[1]:.4f}), test (NDCG@10: {t_test[0]:.4f}, HR@10: {t_test[1]:.4f})\")\n",
-    "    # wandb.log({\"epoch\": epoch, \"loss\": loss.item(), \"valid NDCG@10\" : t_valid[0], \"valid HR@10\" : t_valid[1], \"test NDCG@10\" : t_test[0], \"test HR@10\" : t_test[1]})\n",
-    "    f.write(str(t_valid) + ' ' + str(t_test) + '\\n')\n",
-    "    f.flush()\n",
-    "    t0 = time.time()\n",
-    "    model.train()\n",
-    "\n",
-    "\n",
-    "    if t_valid[0] > best_ndcg:\n",
-    "\n",
-    "        folder = args.train_dir\n",
-    "        fname = f'SASRec_epoch_{int(epoch)}.pth'\n",
-    "        torch.save(model.state_dict(), os.path.join(args.dataset + '_' + args.train_dir, fname))\n",
-    "\n",
-    "        best_ndcg = t_valid[0]"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "Ays1pqRJcj7X"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "args.train_dir"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "9IV2qU08eUoA"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "kJ71pBElZAMw"
-   },
-   "outputs": [],
-   "source": [
-    "fname"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "YTOEme16ZAMx"
-   },
-   "outputs": [],
-   "source": [
-    "[train, valid, test, usernum, itemnum] = copy.deepcopy(dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "mCTaT0q6ZAMx"
-   },
-   "outputs": [],
-   "source": [
-    "len(valid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "JY8uWnbQZAMx"
-   },
-   "outputs": [],
-   "source": [
-    "len(train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "XIlB-PsaZAMx"
-   },
-   "outputs": [],
-   "source": [
-    "train[23783]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "idx"
-   ],
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "id": "RMKHCMydZAMy"
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 6,
-     "status": "ok",
-     "timestamp": 1691554320161,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     },
-     "user_tz": -540
-    },
-    "id": "36yGpn0bfken",
-    "outputId": "e6ef1899-3cec-440b-abaa-b4c603c669fd",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 0, 0], dtype=int32)"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "seq"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "UUYDUUSzfkaY",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "idx = args.maxlen - 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 6,
-     "status": "ok",
-     "timestamp": 1691554334176,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     },
-     "user_tz": -540
-    },
-    "id": "mEytZLvffkW0",
-    "outputId": "52c70557-16a8-4be5-89ca-7a419262e4a8",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "49"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "idx"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "VxkJR8u9ft2A",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "seq[idx] = valid[u][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 3,
-     "status": "ok",
-     "timestamp": 1691554352527,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     },
-     "user_tz": -540
-    },
-    "id": "cDF3kk6hftyb",
-    "outputId": "7a97a1b4-44fd-4995-ff4e-342b68ddb7dd",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
-       "        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
-       "        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0, 78],\n",
-       "      dtype=int32)"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "seq"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "OxApLIHMftu6",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "idx -= 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "1ymZyBDbf2OG",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "train[u]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "JXR-RkS8ftrY",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "for i in reversed(train[u]):\n",
-    "    seq[idx] = i\n",
-    "    idx -= 1\n",
-    "    if idx == -1: break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 3,
-     "status": "ok",
-     "timestamp": 1691554369744,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     },
-     "user_tz": -540
-    },
-    "id": "y8TpzgyeftoG",
-    "outputId": "30470ebf-3824-4dbe-9122-b90452edbf66",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,\n",
-       "       46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62,\n",
-       "       63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78],\n",
-       "      dtype=int32)"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "seq"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "q5eTR4fFftkC",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "rated = set(train[u])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ifhJCb9Iftfh",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "rated"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "CoFAVq8mfkTY",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "rated.add(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "v4uAqf8KfkPh",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "item_idx = [test[u][0]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 3,
-     "status": "ok",
-     "timestamp": 1691554427589,
-     "user": {
-      "displayName": "KYEONGCHAN LEE",
-      "userId": "03106579917275952793"
-     },
-     "user_tz": -540
-    },
-    "id": "EfZENwm6fjnH",
-    "outputId": "9720051c-5856-4f3b-e1f2-856f1e7362b1",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[79]"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "item_idx"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "flN9FCy1ce0H",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "for _ in range(100):\n",
-    "    t = np.random.randint(1, itemnum + 1)\n",
-    "    while t in rated: t = np.random.randint(1, itemnum + 1)\n",
-    "    item_idx.append(t)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "n6Lco1xWgNQu",
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "bTBOLLklgDSc",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "item_idx"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "VZcaSWwEgDYx",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "OINRXapegDcs",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ewNkZWFEgDgJ",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Yr_MJ2dLgDjx",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "SQYsGfPfgDnL",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "V2bQJD8FgDqw",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "background_save": true
-    },
-    "id": "3t9iqc31gDuw",
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "outputId": "65e4a0a4-2732-46e4-9b68-653d31662abe"
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "ERROR:root:Internal Python error in the inspect module.\n",
-      "Below is the traceback from this internal error.\n",
-      "\n",
-      "ERROR:root:Internal Python error in the inspect module.\n",
-      "Below is the traceback from this internal error.\n",
-      "\n",
-      "ERROR:root:Internal Python error in the inspect module.\n",
-      "Below is the traceback from this internal error.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Traceback (most recent call last):\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n",
-      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
-      "  File \"<ipython-input-221-1d7a8df84366>\", line 1, in <cell line: 1>\n",
-      "    torch.LongTensor(positions, device=model.dev)\n",
-      "RuntimeError: legacy constructor expects device type: cpu but device type: cuda was passed\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n",
-      "    stb = value._render_traceback_()\n",
-      "AttributeError: 'RuntimeError' object has no attribute '_render_traceback_'\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1101, in get_records\n",
-      "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 248, in wrapped\n",
-      "    return f(*args, **kwargs)\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 281, in _fixed_getinnerframes\n",
-      "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
-      "  File \"/usr/lib/python3.10/inspect.py\", line 1662, in getinnerframes\n",
-      "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
-      "  File \"/usr/lib/python3.10/inspect.py\", line 1620, in getframeinfo\n",
-      "    filename = getsourcefile(frame) or getfile(frame)\n",
-      "  File \"/usr/lib/python3.10/inspect.py\", line 829, in getsourcefile\n",
-      "    module = getmodule(object, filename)\n",
-      "  File \"/usr/lib/python3.10/inspect.py\", line 861, in getmodule\n",
-      "    file = getabsfile(object, _filename)\n",
-      "  File \"/usr/lib/python3.10/inspect.py\", line 845, in getabsfile\n",
-      "    return os.path.normcase(os.path.abspath(_filename))\n",
-      "  File \"/usr/lib/python3.10/posixpath.py\", line 384, in abspath\n",
-      "    cwd = os.getcwd()\n",
-      "OSError: [Errno 107] Transport endpoint is not connected\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n",
-      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
-      "  File \"<ipython-input-221-1d7a8df84366>\", line 1, in <cell line: 1>\n",
-      "    torch.LongTensor(positions, device=model.dev)\n",
-      "RuntimeError: legacy constructor expects device type: cpu but device type: cuda was passed\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n",
-      "    stb = value._render_traceback_()\n",
-      "AttributeError: 'RuntimeError' object has no attribute '_render_traceback_'\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3473, in run_ast_nodes\n",
-      "    if (await self.run_code(code, result,  async_=asy)):\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3575, in run_code\n",
-      "    self.showtraceback(running_compiled_code=True)\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2101, in showtraceback\n",
-      "    stb = self.InteractiveTB.structured_traceback(etype,\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1367, in structured_traceback\n",
-      "    return FormattedTB.structured_traceback(\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1267, in structured_traceback\n",
-      "    return VerboseTB.structured_traceback(\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1124, in structured_traceback\n",
-      "    formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1082, in format_exception_as_a_whole\n",
-      "    last_unique, recursion_repeat = find_recursion(orig_etype, evalue, records)\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 382, in find_recursion\n",
-      "    return len(records), 0\n",
-      "TypeError: object of type 'NoneType' has no len()\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n",
-      "    stb = value._render_traceback_()\n",
-      "AttributeError: 'TypeError' object has no attribute '_render_traceback_'\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1101, in get_records\n",
-      "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 248, in wrapped\n",
-      "    return f(*args, **kwargs)\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 281, in _fixed_getinnerframes\n",
-      "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
-      "  File \"/usr/lib/python3.10/inspect.py\", line 1662, in getinnerframes\n",
-      "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
-      "  File \"/usr/lib/python3.10/inspect.py\", line 1620, in getframeinfo\n",
-      "    filename = getsourcefile(frame) or getfile(frame)\n",
-      "  File \"/usr/lib/python3.10/inspect.py\", line 829, in getsourcefile\n",
-      "    module = getmodule(object, filename)\n",
-      "  File \"/usr/lib/python3.10/inspect.py\", line 861, in getmodule\n",
-      "    file = getabsfile(object, _filename)\n",
-      "  File \"/usr/lib/python3.10/inspect.py\", line 845, in getabsfile\n",
-      "    return os.path.normcase(os.path.abspath(_filename))\n",
-      "  File \"/usr/lib/python3.10/posixpath.py\", line 384, in abspath\n",
-      "    cwd = os.getcwd()\n",
-      "OSError: [Errno 107] Transport endpoint is not connected\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n",
-      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
-      "  File \"<ipython-input-221-1d7a8df84366>\", line 1, in <cell line: 1>\n",
-      "    torch.LongTensor(positions, device=model.dev)\n",
-      "RuntimeError: legacy constructor expects device type: cpu but device type: cuda was passed\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n",
-      "    stb = value._render_traceback_()\n",
-      "AttributeError: 'RuntimeError' object has no attribute '_render_traceback_'\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3473, in run_ast_nodes\n",
-      "    if (await self.run_code(code, result,  async_=asy)):\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3575, in run_code\n",
-      "    self.showtraceback(running_compiled_code=True)\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2101, in showtraceback\n",
-      "    stb = self.InteractiveTB.structured_traceback(etype,\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1367, in structured_traceback\n",
-      "    return FormattedTB.structured_traceback(\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1267, in structured_traceback\n",
-      "    return VerboseTB.structured_traceback(\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1124, in structured_traceback\n",
-      "    formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1082, in format_exception_as_a_whole\n",
-      "    last_unique, recursion_repeat = find_recursion(orig_etype, evalue, records)\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 382, in find_recursion\n",
-      "    return len(records), 0\n",
-      "TypeError: object of type 'NoneType' has no len()\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n",
-      "    stb = value._render_traceback_()\n",
-      "AttributeError: 'TypeError' object has no attribute '_render_traceback_'\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3030, in _run_cell\n",
-      "    return runner(coro)\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/async_helpers.py\", line 78, in _pseudo_sync_runner\n",
-      "    coro.send(None)\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3257, in run_cell_async\n",
-      "    has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3492, in run_ast_nodes\n",
-      "    self.showtraceback()\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2101, in showtraceback\n",
-      "    stb = self.InteractiveTB.structured_traceback(etype,\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1367, in structured_traceback\n",
-      "    return FormattedTB.structured_traceback(\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1267, in structured_traceback\n",
-      "    return VerboseTB.structured_traceback(\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1142, in structured_traceback\n",
-      "    formatted_exceptions += self.format_exception_as_a_whole(etype, evalue, etb, lines_of_context,\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1082, in format_exception_as_a_whole\n",
-      "    last_unique, recursion_repeat = find_recursion(orig_etype, evalue, records)\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 382, in find_recursion\n",
-      "    return len(records), 0\n",
-      "TypeError: object of type 'NoneType' has no len()\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n",
-      "    stb = value._render_traceback_()\n",
-      "AttributeError: 'TypeError' object has no attribute '_render_traceback_'\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1101, in get_records\n",
-      "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 248, in wrapped\n",
-      "    return f(*args, **kwargs)\n",
-      "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 281, in _fixed_getinnerframes\n",
-      "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
-      "  File \"/usr/lib/python3.10/inspect.py\", line 1662, in getinnerframes\n",
-      "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
-      "  File \"/usr/lib/python3.10/inspect.py\", line 1620, in getframeinfo\n",
-      "    filename = getsourcefile(frame) or getfile(frame)\n",
-      "  File \"/usr/lib/python3.10/inspect.py\", line 829, in getsourcefile\n",
-      "    module = getmodule(object, filename)\n",
-      "  File \"/usr/lib/python3.10/inspect.py\", line 861, in getmodule\n",
-      "    file = getabsfile(object, _filename)\n",
-      "  File \"/usr/lib/python3.10/inspect.py\", line 845, in getabsfile\n",
-      "    return os.path.normcase(os.path.abspath(_filename))\n",
-      "  File \"/usr/lib/python3.10/posixpath.py\", line 384, in abspath\n",
-      "    cwd = os.getcwd()\n",
-      "OSError: [Errno 107] Transport endpoint is not connected\n"
-     ]
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "lJETHzwigDxy",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "kgzKNZfYgD1m",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "tf4_A2JmgD5c",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "_8phDK1ugD9r",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "YSVy5mX-gEBA",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "HX5QFhPJgEEZ",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.9.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
\ No newline at end of file
+{"cells":[{"cell_type":"code","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting pymysql\n","  Downloading PyMySQL-1.1.0-py3-none-any.whl (44 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.8/44.8 kB\u001b[0m \u001b[31m756.1 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: pymysql\n","Successfully installed pymysql-1.1.0\n"]}],"source":["pip install pymysql"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"NKDyPANDcj7D","executionInfo":{"status":"ok","timestamp":1704095209618,"user_tz":-540,"elapsed":8701,"user":{"displayName":"KYEONGCHAN LEE","userId":"03106579917275952793"}},"outputId":"54d454bc-6686-44fa-cb75-901b79591553","colab":{"base_uri":"https://localhost:8080/"}}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["import pymysql"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"WVE63KBbcj7D"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["endpoint = \"pseudorec.cvhv2t0obyv3.ap-northeast-2.rds.amazonaws.com\"\n","port = 3306\n","user = \"admin\"\n","region = \"ap-northeast-2c\"\n","dbname = \"movielens25m\"\n","passwd = 'Precsys1!'\n","\n","# connection = pymysql.connect(host=endpoint, user=user, passwd=passwd, port=port,\n","#                                 database=dbname)"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"xZRkRRYucj7E"}},{"cell_type":"code","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"pycharm":{"name":"#%%\n"},"colab":{"base_uri":"https://localhost:8080/"},"id":"nwzAtR3Ecj7E","executionInfo":{"status":"ok","timestamp":1704096023877,"user_tz":-540,"elapsed":24817,"user":{"displayName":"KYEONGCHAN LEE","userId":"03106579917275952793"}},"outputId":"b9af564e-9c19-4963-d3c8-b1698641760f"}},{"cell_type":"code","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'/content'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":5}],"source":["import os\n","os.getcwd()"],"metadata":{"pycharm":{"name":"#%%\n"},"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"jLYz0Wx9cj7E","executionInfo":{"status":"ok","timestamp":1704096023878,"user_tz":-540,"elapsed":21,"user":{"displayName":"KYEONGCHAN LEE","userId":"03106579917275952793"}},"outputId":"644f47b5-f0f0-4ccf-a0f5-c48e36b82fea"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["import os\n","os.chdir('/content/drive/MyDrive/000GithubRepos/recsys_key_papers_implementation/SASRec')"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"uF4rBkGgcj7E"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["import pandas as pd"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"wqdPtiQucj7E"}},{"cell_type":"code","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['/content',\n"," '/env/python',\n"," '/usr/lib/python310.zip',\n"," '/usr/lib/python3.10',\n"," '/usr/lib/python3.10/lib-dynload',\n"," '',\n"," '/usr/local/lib/python3.10/dist-packages',\n"," '/usr/lib/python3/dist-packages',\n"," '/usr/local/lib/python3.10/dist-packages/IPython/extensions',\n"," '/root/.ipython']"]},"metadata":{},"execution_count":8}],"source":["import sys\n","sys.path"],"metadata":{"pycharm":{"name":"#%%\n"},"colab":{"base_uri":"https://localhost:8080/"},"id":"e7emrLYncj7E","executionInfo":{"status":"ok","timestamp":1704096028671,"user_tz":-540,"elapsed":6,"user":{"displayName":"KYEONGCHAN LEE","userId":"03106579917275952793"}},"outputId":"af4f1390-5a07-479e-b75c-1b2bedc412c1"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["sys.path.append('../')"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"QNnGfwwmcj7E"}},{"cell_type":"code","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting boto3\n","  Downloading boto3-1.34.11-py3-none-any.whl (139 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.3/139.3 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting botocore<1.35.0,>=1.34.11 (from boto3)\n","  Downloading botocore-1.34.11-py3-none-any.whl (11.9 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.9/11.9 MB\u001b[0m \u001b[31m24.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting jmespath<2.0.0,>=0.7.1 (from boto3)\n","  Downloading jmespath-1.0.1-py3-none-any.whl (20 kB)\n","Collecting s3transfer<0.11.0,>=0.10.0 (from boto3)\n","  Downloading s3transfer-0.10.0-py3-none-any.whl (82 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m82.1/82.1 kB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.10/dist-packages (from botocore<1.35.0,>=1.34.11->boto3) (2.8.2)\n","Requirement already satisfied: urllib3<2.1,>=1.25.4 in /usr/local/lib/python3.10/dist-packages (from botocore<1.35.0,>=1.34.11->boto3) (2.0.7)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.35.0,>=1.34.11->boto3) (1.16.0)\n","Installing collected packages: jmespath, botocore, s3transfer, boto3\n","Successfully installed boto3-1.34.11 botocore-1.34.11 jmespath-1.0.1 s3transfer-0.10.0\n"]}],"source":["pip install boto3"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"tqYg8k9_cj7E","outputId":"52e9938c-8aa9-44fc-d450-2584ddf2543a","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1704096043391,"user_tz":-540,"elapsed":9985,"user":{"displayName":"KYEONGCHAN LEE","userId":"03106579917275952793"}}}},{"cell_type":"markdown","source":["# 데이터 불러오기"],"metadata":{"collapsed":false,"pycharm":{"name":"#%% md\n"},"id":"KV0gagCTcj7E"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["import os\n","\n","import boto3\n","import pandas as pd\n","import pymysql\n","from boto3.dynamodb.conditions import Key\n","# from dotenv import load_dotenv\n","\n","# load_dotenv()\n","\n","class MysqlClient:\n","    def __init__(self):\n","        self.endpoint = \"pseudorec.cvhv2t0obyv3.ap-northeast-2.rds.amazonaws.com\"\n","        self.port = 3306\n","        self.user = \"admin\"\n","        self.region = \"ap-northeast-2c\"\n","        self.dbname = \"movielens25m\"\n","        self.passwd = 'Precsys1!'\n","        os.environ['LIBMYSQL_ENABLE_CLEARTEXT_PLUGIN'] = '1'\n","        # self.connection = pymysql.connect(host=endpoint, user=user, passwd=passwd, port=port, database=dbname)\n","\n","    def get_connection(self):\n","        connection = pymysql.connect(host=self.endpoint, user=self.user, passwd=self.passwd, port=self.port,\n","                                     database=self.dbname)\n","        return connection\n","\n","    def get_count(self, table_name):\n","        with self.get_connection().cursor() as cursor:\n","            cursor.execute(f\"select count(*) from {table_name}\")\n","            return cursor.fetchall()[0][0]\n","\n","    def get_movies(self):\n","        with self.get_connection() as connection:\n","            df = pd.read_sql(sql='select * from movies', con=connection)\n","            return df\n","\n","    def get_daum_movies(self):\n","        with self.get_connection() as connection:\n","            df = pd.read_sql(sql='select * from daum_movies', con=connection)\n","            return df\n","\n","    def get_daum_ratings(self):\n","        with self.get_connection() as connection:\n","            df = pd.read_sql(sql='select * from daum_ratings', con=connection)\n","            return df\n","\n","    def get_url(self, title):\n","        with self.get_connection() as connection:\n","            cursor = connection.cursor()\n","            cursor.execute(f\"\"\"\n","            select url from movies where title = '{title}'\n","            \"\"\")\n","            url = cursor.fetchall()[0][0]\n","            return url\n","\n","    def get_table_names(self):\n","        print(\"Tables : \")\n","        with self.get_connection().cursor() as cursor:\n","            sql = \"SHOW TABLES\"\n","            cursor.execute(sql)\n","            result = cursor.fetchall()\n","            for row in result:\n","                print(row[0])\n","\n","    def get_data_type(self, table_name):\n","        with self.get_connection().cursor() as cursor:\n","            cursor.execute(f\"SHOW COLUMNS FROM {table_name}\")\n","            columns = cursor.fetchall()\n","        for column in columns:\n","            column_name = column[0]\n","            data_type = column[1]\n","            print(f\"Column: {column_name}, Data Type: {data_type}\")\n","\n","\n","class DynamoDB:\n","    def __init__(self, table_name: str):\n","        self.resource = boto3.resource(\n","            'dynamodb',\n","            aws_access_key_id=os.environ['AWS_ACCESS_KEY_ID'],\n","            aws_secret_access_key=os.environ['AWS_SECRET_ACCESS_KEY'],\n","            region_name=os.environ['AWS_REGION_NAME'],\n","        )\n","\n","        self.client = boto3.client(\n","            'dynamodb',\n","            aws_access_key_id=os.environ['AWS_ACCESS_KEY_ID'],\n","            aws_secret_access_key=os.environ['AWS_SECRET_ACCESS_KEY'],\n","            region_name=os.environ['AWS_REGION_NAME'],\n","        )\n","        self.table = self.resource.Table(table_name)  # clicklog 테이블 등으로 연결\n","\n","    def put_item(self, click_log):\n","        resp = self.table.put_item(Item=click_log)\n","\n","    def get_a_user_logs(self, user_name: str):\n","        query = {\"KeyConditionExpression\": Key(\"userId\").eq(user_name)}\n","        resp = self.table.query(**query)\n","        return pd.DataFrame(resp['Items'])\n"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"akF5br0Dcj7E"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["mysql = MysqlClient()"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"w7_oitsecj7E"}},{"cell_type":"code","execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-11-22af03e4e8d4>:39: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n","  df = pd.read_sql(sql='select * from daum_movies', con=connection)\n"]}],"source":["daum_movies = mysql.get_daum_movies()"],"metadata":{"pycharm":{"name":"#%%\n"},"colab":{"base_uri":"https://localhost:8080/"},"id":"z4smMQdpcj7E","executionInfo":{"status":"ok","timestamp":1704096244710,"user_tz":-540,"elapsed":1068,"user":{"displayName":"KYEONGCHAN LEE","userId":"03106579917275952793"}},"outputId":"7c9dc9d2-de55-4259-bf1d-f83e62f8954f"}},{"cell_type":"markdown","source":["# 테이블 총 개수"],"metadata":{"collapsed":false,"pycharm":{"name":"#%% md\n"},"id":"LLdujZ3icj7G"}},{"cell_type":"code","execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-11-22af03e4e8d4>:44: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n","  df = pd.read_sql(sql='select * from daum_ratings', con=connection)\n"]}],"source":["daum_ratings = mysql.get_daum_ratings()"],"metadata":{"pycharm":{"name":"#%%\n"},"colab":{"base_uri":"https://localhost:8080/"},"id":"FiIIbDCacj7G","executionInfo":{"status":"ok","timestamp":1704096269330,"user_tz":-540,"elapsed":5370,"user":{"displayName":"KYEONGCHAN LEE","userId":"03106579917275952793"}},"outputId":"fd51ea43-3f0e-4307-be86-bfe29883f295"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["available_user = daum_ratings[daum_ratings['nickName'].map(lambda x: x not in ['휴면 사용자', '', '닉네임을 등록해 주세요', '닉네임'])]"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"dl1kyGkbM0GT"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["merged = pd.merge(left=available_user, right=daum_movies, how='left', on='movieId')[['nickName', 'movieId', 'titleKo','rating',  'timestamp', 'numOfSiteRatings']]"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"ip0yIMgeM0GT"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["merged"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"pQ3Dydq1M0GT"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["average_ratings = merged.groupby('movieId')['rating'].mean().reset_index()"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"L9oBM9_QM0GT"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["rating_mean_dict = dict(zip(average_ratings['movieId'], average_ratings['rating']))"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"T7y42sX-M0GT"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["from collections import Counter\n","rating_num_dict = Counter(merged['movieId'])"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"QbsR1l9eM0GT"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["daum_movies['rating_mean'] = daum_movies['movieId'].map(rating_mean_dict)"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"2JDHtBZDM0GT"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["daum_movies[daum_movies['movieId'] == 128434]"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"EY8e4iBfM0GT"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["daum_movies['num_of_collected_ratings'] = daum_movies['movieId'].map(rating_num_dict)"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"OmBngzUGM0GT"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["daum_ratings[daum_ratings['movieId'] == 128434]['rating'].mean()"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"nLzMqAmTM0GT"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["daum_movies[daum_movies['num_of_collected_ratings']>100].sort_values('rating_mean', ascending=False).head(30)"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"hIMN_vg1M0GU"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["Counter(merged['titleKo']).most_common(10)"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"IJjXwq3iM0GU"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["# # movie_dictionary\n","# movies = pd.read_table('../data/ml-1m_grouplens/movies.dat', sep='::', header=None, names=['movie_id', 'title', 'genres'],\n","#                        engine='python', encoding_errors='ignore')\n","# movie_dict = movies.to_dict('index')"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"Mm7AiRe9M0GU"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["# # movie_dictionary\n","# movies = pd.read_table('../data/ml-1m_grouplens/movies.dat', sep='::', header=None, names=['movie_id', 'title', 'genres'],\n","#                        engine='python', encoding_errors='ignore')\n","# movie_dict = movies.to_dict('index')"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"FrJPrPPGM0GU"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["# ratings = pd.read_table('../data/ml-1m_grouplens/ratings.dat', sep='::', header=None, names=['userid', 'movieid', 'rating', 'timestamp'],\n","#                        engine='python', encoding_errors='ignore')"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"1zS_EUIhM0GU"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["merged.nunique()"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"HjYzcvjKM0GU"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["user_id_dict = {nn:i for i, nn in enumerate(merged['nickName'].unique())}"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"4YIcfuofM0GW"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["movie_id_dict = {site_mid:i for i, site_mid in enumerate(merged['movieId'].unique())}"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"d0mHZk6bM0GW"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["# uid, iid 부여\n","merged['uid'] = merged['nickName'].map(user_id_dict)\n","merged['iid'] = merged['movieId'].map(movie_id_dict)\n","merged"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"jayOzqrbM0GW"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["from datetime import datetime"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"dO9OiKPvM0GW"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["merged['dt'] = merged['timestamp'].map(lambda x: datetime.fromtimestamp(x))"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"szB3AkNDM0GW"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["merged"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"qznIxtPtM0GW"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["sorted_iid_lists = merged.sort_values(by=['uid', 'timestamp']).groupby('uid')['iid'].apply(list)\n","sorted_iid_lists"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"HSuJ3D9gM0GW"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["sorted_iid_dict = dict(sorted_iid_lists)"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"4cPQDnwoM0GW"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["# 시퀀스 평균 길이는?\n","import numpy as np\n","np.mean([len(seq) for uid, seq in sorted_iid_dict.items()])"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"glSfVP1KM0GW"}},{"cell_type":"markdown","source":["# 모델 준비"],"metadata":{"collapsed":false,"pycharm":{"name":"#%% md\n"},"id":"ev73tJx8M0GW"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["import time\n","import torch\n","from torch import nn\n","import argparse\n","from model import SASRec\n","from utils import *"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"g4doBoMmM0GW"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["def str2bool(s):\n","    if s not in {'false', 'true'}:\n","        raise ValueError('Not a valid boolean string')\n","    return s == 'true'\n","\n","def get_args():\n","    parser = argparse.ArgumentParser()\n","    parser.add_argument('--ratings_dir', required=True)\n","    parser.add_argument('--model_output_dir', required=True)\n","    parser.add_argument('--batch_size', default=128, type=int)\n","    parser.add_argument('--lr', default=0.001, type=float)\n","    parser.add_argument('--maxlen', default=50, type=int)\n","    parser.add_argument('--hidden_units', default=50, type=int)\n","    parser.add_argument('--num_blocks', default=2, type=int)\n","    parser.add_argument('--num_epochs', default=200, type=int)\n","    parser.add_argument('--num_heads', default=1, type=int)\n","    parser.add_argument('--dropout_rate', default=0.5, type=float)\n","    parser.add_argument('--l2_emb', default=0.0, type=float)\n","    parser.add_argument('--device', default='cpu', type=str)\n","    parser.add_argument('--inference_only', default=False, type=str2bool)\n","    parser.add_argument('--state_dict_path', default=None, type=str)\n","    return parser.parse_args(args=['--ratings_dir', '../data/ml-1m_grouplens/ratings.dat', '--model_output_dir', 'model_output'])\n","args = get_args()"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"PvitQyz3M0GW"}},{"cell_type":"markdown","source":["# 학습모델 저장 경로"],"metadata":{"collapsed":false,"pycharm":{"name":"#%% md\n"},"id":"zBMpinNCM0GW"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["print(f\"args.model_output_dir : {args.model_output_dir}\")\n","if not os.path.exists(args.model_output_dir):\n","    os.mkdir(args.model_output_dir)"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"QW2Mv2dyM0GW"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["# [str(k) + ',' + str(v)\n","for k, v in sorted(vars(args).items(), key=lambda x: x[0]):\n","    print(f\"{k:30} : {str(v):20}\")"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"XZFpjnb0M0GW"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["args.dataset = 'daum'"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"hG1lNG9dM0GX"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["args.train_dir = 'train_dir'"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"IYhd4-3yM0GX"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["if not os.path.isdir(args.dataset + '_' + args.train_dir):  # 데이터 없다면 폴더 만듦\n","    os.makedirs(args.dataset + '_' + args.train_dir)\n","    print(f\"made {args.dataset + '_' + args.train_dir} folder\")\n","with open(os.path.join(args.dataset + '_' + args.train_dir, 'args.txt'), 'w') as f:  # argument 저장\n","    f.write('\\n'.join([str(k) + ',' + str(v) for k, v in sorted(vars(args).items(), key=lambda x: x[0])]))\n","    print(f\"wrote '{os.path.join(args.dataset + '_' + args.train_dir, 'args.txt')}'\")\n","f.close()"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"bhYMV8X9M0GX"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["from collections import defaultdict"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"qqUw9uPZM0GX"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["# f = open(args.ratings_dir, 'r')\n","# user_seqs = defaultdict(list)\n","# for line in f.readlines():\n","#     data_lst = line.replace('\\n', '').split('::')\n","#     if len(data_lst) < 4:\n","#         continue\n","#     userid, movieid, rating, timestamp = [int(data) for data in line.replace('\\n', '').split('::')]\n","#     user_seqs[userid].append((movieid, timestamp))\n","\n","# f.close()"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"3SyTd-AbM0GX"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["# for userid in list(user_seqs.keys()):\n","#     sorted_user_seq = sorted(user_seqs[userid], key=lambda x: x[1])\n","#     sorted_user_seq = [movieid for movieid, timestamp in sorted_user_seq]\n","#     user_seqs[userid] = sorted_user_seq"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"x3SJUCu8M0GX"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["# from collections import defaultdict\n","\n","# # train/val/test data generation\n","# def data_partition(fname):\n","#     usernum = 0\n","#     itemnum = 0\n","#     User = defaultdict(list)\n","#     user_train = {}\n","#     user_valid = {}\n","#     user_test = {}\n","#     # assume user/item index starting from 1\n","#     f = open(f'{fname}')\n","#     for line in f:\n","#         u, i = line.rstrip().split(' ')\n","#         u = int(u)\n","#         i = int(i)\n","#         usernum = max(u, usernum)\n","#         itemnum = max(i, itemnum)\n","#         User[u].append(i)\n","\n","#     for user in User:\n","#         nfeedback = len(User[user])\n","#         if nfeedback < 3:\n","#             user_train[user] = User[user]\n","#             user_valid[user] = []\n","#             user_test[user] = []\n","#         else:\n","#             user_train[user] = User[user][:-2]\n","#             user_valid[user] = []\n","#             user_valid[user].append(User[user][-2])\n","#             user_test[user] = []\n","#             user_test[user].append(User[user][-1])\n","#     return [user_train, user_valid, user_test, usernum, itemnum]\n","\n","# dataset = data_partition(args.dataset)"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"QNafUM4oM0GX"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["user_train = {}\n","user_valid = {}\n","user_test = {}\n","\n","for uid, seq in sorted_iid_dict.items():\n","    nfeedback = len(seq)\n","    if nfeedback < 3:\n","        user_train[uid] = seq\n","        user_valid[uid] = []\n","        user_test[uid] = []\n","    else:\n","        user_train[uid] = seq[:-2]\n","        user_valid[uid] = []\n","        user_valid[uid].append(seq[-2])\n","        user_test[uid] = []\n","        user_test[uid].append(seq[-1])\n","\n","usernum = merged['uid'].nunique()\n","itemnum = merged['iid'].nunique()\n","dataset = [user_train, user_valid, user_test, usernum, itemnum]"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"o5sj0ZPGM0GX"}},{"cell_type":"markdown","source":["![image.png](data:image/png;base64,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)"],"metadata":{"collapsed":false,"pycharm":{"name":"#%% md\n"},"id":"s1Pgi3nDM0GX"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["print(f\"유저 최소값 : {min(user_train.keys())}\")\n","print(f\"유저 최대값 : {max(user_train.keys()):,}\")\n","print(f\"유저 수 : {len(user_train):,}\")"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"hFXI_7UdM0Gb"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["num_batch = len(user_train) // args.batch_size # tail? + ((len(user_train) % args.batch_size) != 0)\n","cc = 0.0\n","for u in user_train:\n","    cc += len(user_train[u])\n","\n","print(f\"len(user_train) : {len(user_train)}\")\n","print(f\"args.batch_size : {args.batch_size}\")\n","print(f\"num_batch : {num_batch}\")\n","print(f\"average sequence length : {(cc / len(user_train)):.2f}\")"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"gZGrdcoDM0Gb"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["f = open(os.path.join(args.dataset + '_' + args.train_dir, 'log.txt'), 'w')\n","print(f\"logging 위치 : {os.path.join(args.dataset + '_' + args.train_dir, 'log.txt')}\")"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"WLpFvu-MM0Gb"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["sampler = WarpSampler(user_train, usernum, itemnum, batch_size=args.batch_size, maxlen=args.maxlen, n_workers=3)\n","model = SASRec(usernum, itemnum, args).to(args.device) # no ReLU activation in original SASRec implementation?"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"xsSaYmSuPQm8"}},{"cell_type":"code","execution_count":null,"outputs":[],"source":["fname"],"metadata":{"pycharm":{"name":"#%%\n"},"id":"dzmqkqC5PgFE"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"VxkJR8u9ft2A","pycharm":{"name":"#%%\n"}},"outputs":[],"source":["seq[idx] = valid[u][0]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1691554352527,"user":{"displayName":"KYEONGCHAN LEE","userId":"03106579917275952793"},"user_tz":-540},"id":"cDF3kk6hftyb","outputId":"7a97a1b4-44fd-4995-ff4e-342b68ddb7dd","pycharm":{"name":"#%%\n"}},"outputs":[{"data":{"text/plain":["array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n","        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n","        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0, 78],\n","      dtype=int32)"]},"execution_count":64,"metadata":{},"output_type":"execute_result"}],"source":["seq"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OxApLIHMftu6","pycharm":{"name":"#%%\n"}},"outputs":[],"source":["idx -= 1"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1ymZyBDbf2OG","pycharm":{"name":"#%%\n"}},"outputs":[],"source":["train[u]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JXR-RkS8ftrY","pycharm":{"name":"#%%\n"}},"outputs":[],"source":["for i in reversed(train[u]):\n","    seq[idx] = i\n","    idx -= 1\n","    if idx == -1: break"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":832,"status":"ok","timestamp":1704099390065,"user":{"displayName":"KYEONGCHAN LEE","userId":"03106579917275952793"},"user_tz":-540},"id":"y8TpzgyeftoG","outputId":"d5dfe56e-61d9-46b3-8e60-2fa052b314a6","pycharm":{"name":"#%%\n"}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[1131]"]},"metadata":{},"execution_count":74}],"source":["seq"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"q5eTR4fFftkC","pycharm":{"name":"#%%\n"}},"outputs":[],"source":["rated = set(train[u])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ifhJCb9Iftfh","pycharm":{"name":"#%%\n"}},"outputs":[],"source":["rated"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CoFAVq8mfkTY","pycharm":{"name":"#%%\n"}},"outputs":[],"source":["rated.add(0)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"v4uAqf8KfkPh","pycharm":{"name":"#%%\n"}},"outputs":[],"source":["item_idx = [test[u][0]]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1691554427589,"user":{"displayName":"KYEONGCHAN LEE","userId":"03106579917275952793"},"user_tz":-540},"id":"EfZENwm6fjnH","outputId":"9720051c-5856-4f3b-e1f2-856f1e7362b1","pycharm":{"name":"#%%\n"}},"outputs":[{"data":{"text/plain":["[79]"]},"execution_count":73,"metadata":{},"output_type":"execute_result"}],"source":["item_idx"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"flN9FCy1ce0H","pycharm":{"name":"#%%\n"}},"outputs":[],"source":["for _ in range(100):\n","    t = np.random.randint(1, itemnum + 1)\n","    while t in rated: t = np.random.randint(1, itemnum + 1)\n","    item_idx.append(t)"]},{"cell_type":"markdown","metadata":{"id":"n6Lco1xWgNQu","pycharm":{"name":"#%% md\n"}},"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bTBOLLklgDSc","pycharm":{"name":"#%%\n"}},"outputs":[],"source":["item_idx"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VZcaSWwEgDYx","pycharm":{"name":"#%%\n"}},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OINRXapegDcs","pycharm":{"name":"#%%\n"}},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ewNkZWFEgDgJ","pycharm":{"name":"#%%\n"}},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Yr_MJ2dLgDjx","pycharm":{"name":"#%%\n"}},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SQYsGfPfgDnL","pycharm":{"name":"#%%\n"}},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"V2bQJD8FgDqw","pycharm":{"name":"#%%\n"}},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"3t9iqc31gDuw","pycharm":{"name":"#%%\n"},"outputId":"65e4a0a4-2732-46e4-9b68-653d31662abe"},"outputs":[{"name":"stderr","output_type":"stream","text":["ERROR:root:Internal Python error in the inspect module.\n","Below is the traceback from this internal error.\n","\n","ERROR:root:Internal Python error in the inspect module.\n","Below is the traceback from this internal error.\n","\n","ERROR:root:Internal Python error in the inspect module.\n","Below is the traceback from this internal error.\n","\n"]},{"name":"stdout","output_type":"stream","text":["Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n","    exec(code_obj, self.user_global_ns, self.user_ns)\n","  File \"<ipython-input-221-1d7a8df84366>\", line 1, in <cell line: 1>\n","    torch.LongTensor(positions, device=model.dev)\n","RuntimeError: legacy constructor expects device type: cpu but device type: cuda was passed\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n","    stb = value._render_traceback_()\n","AttributeError: 'RuntimeError' object has no attribute '_render_traceback_'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1101, in get_records\n","    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 248, in wrapped\n","    return f(*args, **kwargs)\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 281, in _fixed_getinnerframes\n","    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n","  File \"/usr/lib/python3.10/inspect.py\", line 1662, in getinnerframes\n","    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n","  File \"/usr/lib/python3.10/inspect.py\", line 1620, in getframeinfo\n","    filename = getsourcefile(frame) or getfile(frame)\n","  File \"/usr/lib/python3.10/inspect.py\", line 829, in getsourcefile\n","    module = getmodule(object, filename)\n","  File \"/usr/lib/python3.10/inspect.py\", line 861, in getmodule\n","    file = getabsfile(object, _filename)\n","  File \"/usr/lib/python3.10/inspect.py\", line 845, in getabsfile\n","    return os.path.normcase(os.path.abspath(_filename))\n","  File \"/usr/lib/python3.10/posixpath.py\", line 384, in abspath\n","    cwd = os.getcwd()\n","OSError: [Errno 107] Transport endpoint is not connected\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n","    exec(code_obj, self.user_global_ns, self.user_ns)\n","  File \"<ipython-input-221-1d7a8df84366>\", line 1, in <cell line: 1>\n","    torch.LongTensor(positions, device=model.dev)\n","RuntimeError: legacy constructor expects device type: cpu but device type: cuda was passed\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n","    stb = value._render_traceback_()\n","AttributeError: 'RuntimeError' object has no attribute '_render_traceback_'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3473, in run_ast_nodes\n","    if (await self.run_code(code, result,  async_=asy)):\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3575, in run_code\n","    self.showtraceback(running_compiled_code=True)\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2101, in showtraceback\n","    stb = self.InteractiveTB.structured_traceback(etype,\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1367, in structured_traceback\n","    return FormattedTB.structured_traceback(\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1267, in structured_traceback\n","    return VerboseTB.structured_traceback(\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1124, in structured_traceback\n","    formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1082, in format_exception_as_a_whole\n","    last_unique, recursion_repeat = find_recursion(orig_etype, evalue, records)\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 382, in find_recursion\n","    return len(records), 0\n","TypeError: object of type 'NoneType' has no len()\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n","    stb = value._render_traceback_()\n","AttributeError: 'TypeError' object has no attribute '_render_traceback_'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1101, in get_records\n","    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 248, in wrapped\n","    return f(*args, **kwargs)\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 281, in _fixed_getinnerframes\n","    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n","  File \"/usr/lib/python3.10/inspect.py\", line 1662, in getinnerframes\n","    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n","  File \"/usr/lib/python3.10/inspect.py\", line 1620, in getframeinfo\n","    filename = getsourcefile(frame) or getfile(frame)\n","  File \"/usr/lib/python3.10/inspect.py\", line 829, in getsourcefile\n","    module = getmodule(object, filename)\n","  File \"/usr/lib/python3.10/inspect.py\", line 861, in getmodule\n","    file = getabsfile(object, _filename)\n","  File \"/usr/lib/python3.10/inspect.py\", line 845, in getabsfile\n","    return os.path.normcase(os.path.abspath(_filename))\n","  File \"/usr/lib/python3.10/posixpath.py\", line 384, in abspath\n","    cwd = os.getcwd()\n","OSError: [Errno 107] Transport endpoint is not connected\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n","    exec(code_obj, self.user_global_ns, self.user_ns)\n","  File \"<ipython-input-221-1d7a8df84366>\", line 1, in <cell line: 1>\n","    torch.LongTensor(positions, device=model.dev)\n","RuntimeError: legacy constructor expects device type: cpu but device type: cuda was passed\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n","    stb = value._render_traceback_()\n","AttributeError: 'RuntimeError' object has no attribute '_render_traceback_'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3473, in run_ast_nodes\n","    if (await self.run_code(code, result,  async_=asy)):\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3575, in run_code\n","    self.showtraceback(running_compiled_code=True)\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2101, in showtraceback\n","    stb = self.InteractiveTB.structured_traceback(etype,\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1367, in structured_traceback\n","    return FormattedTB.structured_traceback(\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1267, in structured_traceback\n","    return VerboseTB.structured_traceback(\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1124, in structured_traceback\n","    formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1082, in format_exception_as_a_whole\n","    last_unique, recursion_repeat = find_recursion(orig_etype, evalue, records)\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 382, in find_recursion\n","    return len(records), 0\n","TypeError: object of type 'NoneType' has no len()\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n","    stb = value._render_traceback_()\n","AttributeError: 'TypeError' object has no attribute '_render_traceback_'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3030, in _run_cell\n","    return runner(coro)\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/async_helpers.py\", line 78, in _pseudo_sync_runner\n","    coro.send(None)\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3257, in run_cell_async\n","    has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3492, in run_ast_nodes\n","    self.showtraceback()\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2101, in showtraceback\n","    stb = self.InteractiveTB.structured_traceback(etype,\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1367, in structured_traceback\n","    return FormattedTB.structured_traceback(\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1267, in structured_traceback\n","    return VerboseTB.structured_traceback(\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1142, in structured_traceback\n","    formatted_exceptions += self.format_exception_as_a_whole(etype, evalue, etb, lines_of_context,\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1082, in format_exception_as_a_whole\n","    last_unique, recursion_repeat = find_recursion(orig_etype, evalue, records)\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 382, in find_recursion\n","    return len(records), 0\n","TypeError: object of type 'NoneType' has no len()\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n","    stb = value._render_traceback_()\n","AttributeError: 'TypeError' object has no attribute '_render_traceback_'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1101, in get_records\n","    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 248, in wrapped\n","    return f(*args, **kwargs)\n","  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 281, in _fixed_getinnerframes\n","    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n","  File \"/usr/lib/python3.10/inspect.py\", line 1662, in getinnerframes\n","    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n","  File \"/usr/lib/python3.10/inspect.py\", line 1620, in getframeinfo\n","    filename = getsourcefile(frame) or getfile(frame)\n","  File \"/usr/lib/python3.10/inspect.py\", line 829, in getsourcefile\n","    module = getmodule(object, filename)\n","  File \"/usr/lib/python3.10/inspect.py\", line 861, in getmodule\n","    file = getabsfile(object, _filename)\n","  File \"/usr/lib/python3.10/inspect.py\", line 845, in getabsfile\n","    return os.path.normcase(os.path.abspath(_filename))\n","  File \"/usr/lib/python3.10/posixpath.py\", line 384, in abspath\n","    cwd = os.getcwd()\n","OSError: [Errno 107] Transport endpoint is not connected\n"]}],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lJETHzwigDxy","pycharm":{"name":"#%%\n"}},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kgzKNZfYgD1m","pycharm":{"name":"#%%\n"}},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"tf4_A2JmgD5c","pycharm":{"name":"#%%\n"}},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_8phDK1ugD9r","pycharm":{"name":"#%%\n"}},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YSVy5mX-gEBA","pycharm":{"name":"#%%\n"}},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HX5QFhPJgEEZ","pycharm":{"name":"#%%\n"}},"outputs":[],"source":[]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","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":"3.9.9"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file