[E-08]Learning_Text_Summarisation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "czech-newark",
   "metadata": {},
   "source": [
    "## 학습목표\n",
    "\n",
    "- Extractive/Abstractive summarization 이해하기\n",
    "- 단어장 크기를 줄이는 다양한 text normalization 적용해보기\n",
    "- seq2seq의 성능을 Up시키는 Attention Mechanism 적용하기"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "olympic-influence",
   "metadata": {},
   "source": [
    "### 오늘 실습 예정인 개념들\n",
    "- seq2seq를 사용합니다.\n",
    "- RNN 계열 중 LSTM을 사용하므로 hidden state뿐만 아니라 cell state도 사용해야 합니다.\n",
    "- 디코더의 예측 시퀀스에는 시작 토큰 SOS와 예측 토큰 EOS를 시퀀스의 앞, 뒤로 붙입니다.\n",
    "- seq2seq를 구동시키면 디코더는 시작 토큰을 입력받아 예측을 시작합니다.\n",
    "- seq2seq 기본 모델과 달리, 어텐션 메커니즘을 이용해 인코더의 hidden state의 중요도를 취합한 컨텍스트 벡터를 디코더 스텝별로 계산합니다.\n",
    "- 계산된 컨텍스트 벡터를 이용해서 디코더는 다음 등장할 단어를 예측합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "behavioral-concord",
   "metadata": {},
   "source": [
    "### 데이터 준비하기"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "electrical-barcelona",
   "metadata": {},
   "source": [
    "> 그리고 NLTK 라이브러리에 이미 등록된 불용어(stopwords)_-분석에 별로 의미 없는 단어들-_ 을 사용할 예정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "informed-lounge",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /aiffel/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=3\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "from nltk.corpus import stopwords\n",
    "from bs4 import BeautifulSoup \n",
    "from tensorflow.keras.preprocessing.text import Tokenizer \n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "import urllib.request\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning, module='bs4')\n",
    "\n",
    "print('=3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ahead-bailey",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "전체 샘플수 : 100000\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(os.getenv(\"HOME\")+\"/aiffel/news_summarization/data/Reviews.csv\", nrows=100000)\n",
    "# 약 56만개의 샘플이 있지만 10만개만 사용 예정\n",
    "\n",
    "print('전체 샘플수 :', (len(data)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "analyzed-mirror",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>B001E4KFG0</td>\n",
       "      <td>A3SGXH7AUHU8GW</td>\n",
       "      <td>delmartian</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1303862400</td>\n",
       "      <td>Good Quality Dog Food</td>\n",
       "      <td>I have bought several of the Vitality canned d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B00813GRG4</td>\n",
       "      <td>A1D87F6ZCVE5NK</td>\n",
       "      <td>dll pa</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1346976000</td>\n",
       "      <td>Not as Advertised</td>\n",
       "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>B000LQOCH0</td>\n",
       "      <td>ABXLMWJIXXAIN</td>\n",
       "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1219017600</td>\n",
       "      <td>\"Delight\" says it all</td>\n",
       "      <td>This is a confection that has been around a fe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>B000UA0QIQ</td>\n",
       "      <td>A395BORC6FGVXV</td>\n",
       "      <td>Karl</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1307923200</td>\n",
       "      <td>Cough Medicine</td>\n",
       "      <td>If you are looking for the secret ingredient i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>B006K2ZZ7K</td>\n",
       "      <td>A1UQRSCLF8GW1T</td>\n",
       "      <td>Michael D. Bigham \"M. Wassir\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1350777600</td>\n",
       "      <td>Great taffy</td>\n",
       "      <td>Great taffy at a great price.  There was a wid...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id   ProductId          UserId                      ProfileName  \\\n",
       "0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
       "1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
       "2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
       "3   4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n",
       "4   5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     1                       1      5  1303862400   \n",
       "1                     0                       0      1  1346976000   \n",
       "2                     1                       1      4  1219017600   \n",
       "3                     3                       3      2  1307923200   \n",
       "4                     0                       0      5  1350777600   \n",
       "\n",
       "                 Summary                                               Text  \n",
       "0  Good Quality Dog Food  I have bought several of the Vitality canned d...  \n",
       "1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...  \n",
       "2  \"Delight\" says it all  This is a confection that has been around a fe...  \n",
       "3         Cough Medicine  If you are looking for the secret ingredient i...  \n",
       "4            Great taffy  Great taffy at a great price.  There was a wid...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "inclusive-filename",
   "metadata": {},
   "source": [
    "> 필요한 컬럼만 따로 저장해서 사용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "artificial-vertical",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>Text</th>\n",
       "      <th>Summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52679</th>\n",
       "      <td>Nothing makes me love fall more then pumpkin p...</td>\n",
       "      <td>Liquid Pie!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42147</th>\n",
       "      <td>The millet flour was rancid, I wonder how long...</td>\n",
       "      <td>Rancid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89053</th>\n",
       "      <td>I have purchased these nuts a couple times. Th...</td>\n",
       "      <td>Fairly good value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94000</th>\n",
       "      <td>These Dried Dog Treats are great.  My dog was ...</td>\n",
       "      <td>Great for Diabetic Dog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6581</th>\n",
       "      <td>Great seller. The product was as described.  T...</td>\n",
       "      <td>Good stuff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80632</th>\n",
       "      <td>I was somewhat dubious when I ordered these so...</td>\n",
       "      <td>Just Like From a Candy Store!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46117</th>\n",
       "      <td>This is good loose leaf tea and the packaging ...</td>\n",
       "      <td>Good tea.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69604</th>\n",
       "      <td>I've tried a variety of Earl Grey blends, and ...</td>\n",
       "      <td>Unique Earl Grey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41047</th>\n",
       "      <td>I am very hard on food though, just ask my hus...</td>\n",
       "      <td>Pretty good, maybe not excellent =)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63192</th>\n",
       "      <td>Well being a junk food junkie this is an easy ...</td>\n",
       "      <td>Tasty Snack!!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39471</th>\n",
       "      <td>This is my favorite flavor, with Tully's Frenc...</td>\n",
       "      <td>Best Taste for me</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18623</th>\n",
       "      <td>Yes, I tasted them - they taste like chemicals...</td>\n",
       "      <td>Tastes like chemicals...my Shih tzu &amp; Pekinges...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63210</th>\n",
       "      <td>These are good, but not as good as I'd hoped. ...</td>\n",
       "      <td>It Won't Replace the Cheddar Cheez-it</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11185</th>\n",
       "      <td>I am very pleased with the light crispy textur...</td>\n",
       "      <td>Light and crispy cookie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50401</th>\n",
       "      <td>Why oh why did Altoids mess with something tha...</td>\n",
       "      <td>Awful new taste for Altoids Spearmint</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Text  \\\n",
       "52679  Nothing makes me love fall more then pumpkin p...   \n",
       "42147  The millet flour was rancid, I wonder how long...   \n",
       "89053  I have purchased these nuts a couple times. Th...   \n",
       "94000  These Dried Dog Treats are great.  My dog was ...   \n",
       "6581   Great seller. The product was as described.  T...   \n",
       "80632  I was somewhat dubious when I ordered these so...   \n",
       "46117  This is good loose leaf tea and the packaging ...   \n",
       "69604  I've tried a variety of Earl Grey blends, and ...   \n",
       "41047  I am very hard on food though, just ask my hus...   \n",
       "63192  Well being a junk food junkie this is an easy ...   \n",
       "39471  This is my favorite flavor, with Tully's Frenc...   \n",
       "18623  Yes, I tasted them - they taste like chemicals...   \n",
       "63210  These are good, but not as good as I'd hoped. ...   \n",
       "11185  I am very pleased with the light crispy textur...   \n",
       "50401  Why oh why did Altoids mess with something tha...   \n",
       "\n",
       "                                                 Summary  \n",
       "52679                                        Liquid Pie!  \n",
       "42147                                             Rancid  \n",
       "89053                                  Fairly good value  \n",
       "94000                             Great for Diabetic Dog  \n",
       "6581                                          Good stuff  \n",
       "80632                      Just Like From a Candy Store!  \n",
       "46117                                          Good tea.  \n",
       "69604                                   Unique Earl Grey  \n",
       "41047                Pretty good, maybe not excellent =)  \n",
       "63192                                      Tasty Snack!!  \n",
       "39471                                  Best Taste for me  \n",
       "18623  Tastes like chemicals...my Shih tzu & Pekinges...  \n",
       "63210              It Won't Replace the Cheddar Cheez-it  \n",
       "11185                            Light and crispy cookie  \n",
       "50401              Awful new taste for Altoids Spearmint  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[['Text','Summary']]\n",
    "data.head()\n",
    "\n",
    "#랜덤한 15개 샘플 출력\n",
    "data.sample(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "russian-simulation",
   "metadata": {},
   "source": [
    "### 데이터 전처리하기"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rapid-array",
   "metadata": {},
   "source": [
    "### (1) 중복값, Null이 있는 샘플 제거"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "equipped-capital",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text 열에서 중복을 배제한 유일한 샘플의 수 : 88426\n",
      "Summary 열에서 중복을 배제한 유일한 샘플의 수 : 72348\n"
     ]
    }
   ],
   "source": [
    "print('Text 열에서 중복을 배제한 유일한 샘플의 수 :', data['Text'].nunique())\n",
    "print('Summary 열에서 중복을 배제한 유일한 샘플의 수 :', data['Summary'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "religious-joseph",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100000\n",
      "11574\n",
      "88426\n"
     ]
    }
   ],
   "source": [
    "print(len(data['Text']))\n",
    "print(data['Text'].duplicated().sum())\n",
    "print(len(data['Text'])-data['Text'].duplicated().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "thirty-provider",
   "metadata": {},
   "source": [
    "> summary는 중복이 있을 수 있지만, Text 부분의 중복은 진짜 중복이니 삭제"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "adolescent-turning",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "전체 샘플수 : 88426\n"
     ]
    }
   ],
   "source": [
    "# inplace=True 를 설정하면 DataFrame 타입 값을 return 하지 않고 data 내부를 직접적으로 바꿉니다\n",
    "data.drop_duplicates(subset = ['Text'], inplace=True)\n",
    "print('전체 샘플수 :', (len(data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prostate-bunny",
   "metadata": {},
   "source": [
    "> 근데 Null 값이 중복으로 사라져도 1개는 있을 수 있으니 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "northern-gates",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text       0\n",
      "Summary    1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "amber-shark",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "전체 샘플수 : 88425\n"
     ]
    }
   ],
   "source": [
    "data.dropna(axis=0, inplace=True)\n",
    "print('전체 샘플수 :', (len(data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "heated-thermal",
   "metadata": {},
   "source": [
    "#### 텍스트 정규화와 불용어 제거\n",
    "-  `it'll`은 `it will`과 같고, `mustn't`과 `must not`은 같다.\n",
    "- 이러한 걸 미리 줄여주면 연산량이 줄어든다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "adolescent-challenge",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정규화 사전의 수:  120\n"
     ]
    }
   ],
   "source": [
    "contractions = {\"ain't\": \"is not\", \"aren't\": \"are not\",\"can't\": \"cannot\", \"'cause\": \"because\", \"could've\": \"could have\", \"couldn't\": \"could not\",\n",
    "                           \"didn't\": \"did not\",  \"doesn't\": \"does not\", \"don't\": \"do not\", \"hadn't\": \"had not\", \"hasn't\": \"has not\", \"haven't\": \"have not\",\n",
    "                           \"he'd\": \"he would\",\"he'll\": \"he will\", \"he's\": \"he is\", \"how'd\": \"how did\", \"how'd'y\": \"how do you\", \"how'll\": \"how will\", \"how's\": \"how is\",\n",
    "                           \"I'd\": \"I would\", \"I'd've\": \"I would have\", \"I'll\": \"I will\", \"I'll've\": \"I will have\",\"I'm\": \"I am\", \"I've\": \"I have\", \"i'd\": \"i would\",\n",
    "                           \"i'd've\": \"i would have\", \"i'll\": \"i will\",  \"i'll've\": \"i will have\",\"i'm\": \"i am\", \"i've\": \"i have\", \"isn't\": \"is not\", \"it'd\": \"it would\",\n",
    "                           \"it'd've\": \"it would have\", \"it'll\": \"it will\", \"it'll've\": \"it will have\",\"it's\": \"it is\", \"let's\": \"let us\", \"ma'am\": \"madam\",\n",
    "                           \"mayn't\": \"may not\", \"might've\": \"might have\",\"mightn't\": \"might not\",\"mightn't've\": \"might not have\", \"must've\": \"must have\",\n",
    "                           \"mustn't\": \"must not\", \"mustn't've\": \"must not have\", \"needn't\": \"need not\", \"needn't've\": \"need not have\",\"o'clock\": \"of the clock\",\n",
    "                           \"oughtn't\": \"ought not\", \"oughtn't've\": \"ought not have\", \"shan't\": \"shall not\", \"sha'n't\": \"shall not\", \"shan't've\": \"shall not have\",\n",
    "                           \"she'd\": \"she would\", \"she'd've\": \"she would have\", \"she'll\": \"she will\", \"she'll've\": \"she will have\", \"she's\": \"she is\",\n",
    "                           \"should've\": \"should have\", \"shouldn't\": \"should not\", \"shouldn't've\": \"should not have\", \"so've\": \"so have\",\"so's\": \"so as\",\n",
    "                           \"this's\": \"this is\",\"that'd\": \"that would\", \"that'd've\": \"that would have\", \"that's\": \"that is\", \"there'd\": \"there would\",\n",
    "                           \"there'd've\": \"there would have\", \"there's\": \"there is\", \"here's\": \"here is\",\"they'd\": \"they would\", \"they'd've\": \"they would have\",\n",
    "                           \"they'll\": \"they will\", \"they'll've\": \"they will have\", \"they're\": \"they are\", \"they've\": \"they have\", \"to've\": \"to have\",\n",
    "                           \"wasn't\": \"was not\", \"we'd\": \"we would\", \"we'd've\": \"we would have\", \"we'll\": \"we will\", \"we'll've\": \"we will have\", \"we're\": \"we are\",\n",
    "                           \"we've\": \"we have\", \"weren't\": \"were not\", \"what'll\": \"what will\", \"what'll've\": \"what will have\", \"what're\": \"what are\",\n",
    "                           \"what's\": \"what is\", \"what've\": \"what have\", \"when's\": \"when is\", \"when've\": \"when have\", \"where'd\": \"where did\", \"where's\": \"where is\",\n",
    "                           \"where've\": \"where have\", \"who'll\": \"who will\", \"who'll've\": \"who will have\", \"who's\": \"who is\", \"who've\": \"who have\",\n",
    "                           \"why's\": \"why is\", \"why've\": \"why have\", \"will've\": \"will have\", \"won't\": \"will not\", \"won't've\": \"will not have\",\n",
    "                           \"would've\": \"would have\", \"wouldn't\": \"would not\", \"wouldn't've\": \"would not have\", \"y'all\": \"you all\",\n",
    "                           \"y'all'd\": \"you all would\",\"y'all'd've\": \"you all would have\",\"y'all're\": \"you all are\",\"y'all've\": \"you all have\",\n",
    "                           \"you'd\": \"you would\", \"you'd've\": \"you would have\", \"you'll\": \"you will\", \"you'll've\": \"you will have\",\n",
    "                           \"you're\": \"you are\", \"you've\": \"you have\"}\n",
    "\n",
    "print(\"정규화 사전의 수: \", len(contractions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "forbidden-cement",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "불용어 개수 : 179\n",
      "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n"
     ]
    }
   ],
   "source": [
    "# 불용어 없애주기\n",
    "print('불용어 개수 :', len(stopwords.words('english') ))\n",
    "print(stopwords.words('english'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "spanish-attraction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<WordListCorpusReader in '/aiffel/nltk_data/corpora/stopwords'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "recognized-spectrum",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(stopwords.words())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dense-softball",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['إذ', 'إذا', 'إذما', 'إذن', 'أف']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopwords.words()[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "foreign-selection",
   "metadata": {},
   "source": [
    "> `stopwords.words()`만 하면 다 리스트로 불러오고, 사실 `words()` 안에 path를 넣으면 path에 저장된 게 불려져 오는 것 같다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "through-cassette",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=3\n"
     ]
    }
   ],
   "source": [
    "# 데이터 전처리 함수\n",
    "def preprocess_sentence(sentence, remove_stopwords=True):\n",
    "    sentence = sentence.lower() # 텍스트 소문자화\n",
    "    sentence = BeautifulSoup(sentence, \"lxml\").text # <br />, <a href = ...> 등의 html 태그 제거\n",
    "    sentence = re.sub(r'\\([^)]*\\)', '', sentence) # 괄호로 닫힌 문자열 (...) 제거 Ex) my husband (and myself!) for => my husband for\n",
    "    sentence = re.sub('\"','', sentence) # 쌍따옴표 \" 제거\n",
    "    sentence = ' '.join([contractions[t] if t in contractions else t for t in sentence.split(\" \")]) # 약어 정규화\n",
    "    sentence = re.sub(r\"'s\\b\",\"\", sentence) # 소유격 제거. Ex) roland's -> roland\n",
    "    sentence = re.sub(\"[^a-zA-Z]\", \" \", sentence) # 영어 외 문자(숫자, 특수문자 등) 공백으로 변환\n",
    "    sentence = re.sub('[m]{2,}', 'mm', sentence) # m이 3개 이상이면 2개로 변경. Ex) ummmmmmm yeah -> umm yeah\n",
    "    \n",
    "    # 불용어 제거 (Text)\n",
    "    if remove_stopwords:\n",
    "        tokens = ' '.join(word for word in sentence.split() if not word in stopwords.words('english') if len(word) > 1)\n",
    "    # 불용어 미제거 (Summary)\n",
    "    else:\n",
    "        tokens = ' '.join(word for word in sentence.split() if len(word) > 1)\n",
    "    return tokens\n",
    "print('=3')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "challenging-mouth",
   "metadata": {},
   "source": [
    "> 불용어 미제거는 추후 target 데이터로 쓰일 summary 부분에 영향을 끼친다.  \n",
    "> 거기는 불용어가 있는 게 더 자연스러우니까."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "exterior-kidney",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "text:  everything bought great infact ordered twice third ordered wasfor mother father\n",
      "summary: great way to start the day\n"
     ]
    }
   ],
   "source": [
    "# 임의의 text로 확인해보기\n",
    "temp_text = 'Everything I bought was great, infact I ordered twice and the third ordered was<br />for my mother and father.'\n",
    "temp_summary = 'Great way to start (or finish) the day!!!'\n",
    "\n",
    "print(\"text: \", preprocess_sentence(temp_text))\n",
    "print(\"summary:\", preprocess_sentence(temp_summary, False))  # 불용어를 제거하지 않습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "pending-movement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'write like order test function efficiency would produce'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 또 test\n",
    "test_text = \"If I write   (efe) #$# like this in order to test the function's efficiency, what would it produce?\"\n",
    "preprocess_sentence(test_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "positive-magazine",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text 전처리 후 결과:  ['bought several vitality canned dog food products found good quality product looks like stew processed meat smells better labrador finicky appreciates product better', 'product arrived labeled jumbo salted peanuts peanuts actually small sized unsalted sure error vendor intended represent product jumbo', 'confection around centuries light pillowy citrus gelatin nuts case filberts cut tiny squares liberally coated powdered sugar tiny mouthful heaven chewy flavorful highly recommend yummy treat familiar story lewis lion witch wardrobe treat seduces edmund selling brother sisters witch', 'looking secret ingredient robitussin believe found got addition root beer extract ordered made cherry soda flavor medicinal', 'great taffy great price wide assortment yummy taffy delivery quick taffy lover deal']\n"
     ]
    }
   ],
   "source": [
    "clean_text = []\n",
    "# 전체 Text 데이터에 대한 전처리 : 10분 이상 시간이 걸릴 수 있습니다. \n",
    "for s in data['Text']:\n",
    "    clean_text.append(preprocess_sentence(s))\n",
    "\n",
    "# 전처리 후 출력\n",
    "print(\"Text 전처리 후 결과: \", clean_text[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "devoted-christian",
   "metadata": {},
   "source": [
    "> summary는 stopwords 사용 안 하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "referenced-enlargement",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary 전처리 후 결과:  ['good quality dog food', 'not as advertised', 'delight says it all', 'cough medicine', 'great taffy']\n"
     ]
    }
   ],
   "source": [
    "clean_summary = []\n",
    "# 전체 Summary 데이터에 대한 전처리 : 5분 이상 시간이 걸릴 수 있습니다. \n",
    "for s in data['Summary']:\n",
    "    clean_summary.append(preprocess_sentence(s, False))\n",
    "\n",
    "print(\"Summary 전처리 후 결과: \", clean_summary[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "increased-cloud",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=3\n"
     ]
    }
   ],
   "source": [
    "data['Text'] = clean_text\n",
    "data['Summary'] = clean_summary\n",
    "\n",
    "# 빈 값을 Null 값으로 변환\n",
    "data.replace('', np.nan, inplace=True)\n",
    "print('=3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "norman-holmes",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text        0\n",
       "Summary    70\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Null값 된 거 있는지 확인\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "dental-lexington",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "전체 샘플수 : 88355\n"
     ]
    }
   ],
   "source": [
    "data.dropna(axis=0, inplace=True)\n",
    "print('전체 샘플수 :', (len(data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sonic-denver",
   "metadata": {},
   "source": [
    "### (2) 훈련데이터와 테스트데이터 나누기"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accepting-pilot",
   "metadata": {},
   "source": [
    "#### 샘플의 최대 길이 정하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "mature-family",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "텍스트의 최소 길이 : 2\n",
      "텍스트의 최대 길이 : 1235\n",
      "텍스트의 평균 길이 : 38.792428272310566\n",
      "요약의 최소 길이 : 1\n",
      "요약의 최대 길이 : 28\n",
      "요약의 평균 길이 : 4.010729443721352\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YXNm0zMysHhQpIi9JmkD2zo5bUmzXyqVkZmb1okgROR34e2BqRDwmaQTw88qmZWZm9aBI76wHgfNy848B36xkUmZmVh+KXImYmZl1yUXEzMxK67aISPp5+j6/7M4lzZS0TtLSXGyApPmSlqfv/ikuSdMltUtaLOmQ3DaT0vrLJU3KxQ+VtCRtM13pxe1mZlYdPbWJHCppX+ATkq4me9DwFRGxocD+rwJ+AFydi00B7oiIaZKmpPkLgePJhpsfCRwBXA4cIWkAcDHQCgSwUNKciHg6rXMmcC8wFxgL3FYgr4bSMuXWHpevnHZilTIxs2bT0+2sHwN3AO8EFnb6tBXZeXrnSOdiMw6YlaZnASfl4ldH5h5gb0mDgeOA+RGxIRWO+cDYtGyviLgnIoKsUJ2EmZlVTbdFJCKmR8S7gJkRsV9EjMh99uvFMQdFxJo0/SQwKE0PAZ7IrbcqxXqKr+oivg1JkyW1SWpbv359L1I3M7O8Il18Py3pvcD7UujuiFjcFwePiJAUfbGv7RxnBtmQLbS2tlb8eGZmzaLIAIznAdcAb02fayR9phfHXJtuRZG+16X4arJxuToMTbGe4kO7iJuZWZUU6eL7SeCIiPhqRHyV7PW4Z/bimHPIhlAhfd+ci09MvbSOBJ5Nt73mAcdK6p96ch0LzEvLNko6MvXKmpjbl5mZVUGRl1IJ2Jqb30qnnlrdbihdB4wB9pG0iqyX1TRgtqQzyF5udUpafS5wAtAOvEA23AoRsUHS14AFab1Lcz3DzibrAbYHWa+spuuZZWZWS0WKyM+AeyXdlOZPAq4ssvOImNDNoqO7WDeAc7rZz0xgZhfxNuCgIrmYmVnfK9Kwfpmku4DRKXR6RDxQ0azMzKwuFLkSISLuB+6vcC5mZlZnPHaWmZmV5iJiZmal9VhEJPWTdGe1kjEzs/rSYxGJiK3Ay5LeXKV8zMysjhRpWH8OWCJpPvB8RzAizut+k8a0vdFyzcyaTZEicmP6mJmZvUaR50RmSdoDGB4Rj1QhJzMzqxNFBmD878Ai4NdpfpSkORXOy8zM6kCRLr6XAIcDzwBExCKgN+8TMTOzBlGkiLwUEc92ir1ciWTMzKy+FGlYXybpY0A/SSOB84A/VjYtMzOrB0WuRD4DvBvYDFwHbAQ+W8GczMysThTpnfUC8GVJ38xmY1Pl0zIzs3pQpHfWYZKWAIvJHjr8v5IOrXxqZma2syvSJnIlcHZE/A5A0miyF1W9p5KJmZnZzq9Im8jWjgICEBG/B7ZULiUzM6sX3RYRSYdIOgT4raSfSBoj6f2SfgTcVfaAkg6QtCj32Sjps5IukbQ6Fz8ht81FktolPSLpuFx8bIq1S5pSNiczMyunp9tZ3+00f3FuOsoeMA2dMgqyoeaB1cBNwOnA9yLiO/n1JR0IjCfrIbYv8BtJ70iLfwgcA6wCFkiaExEPls3NzMx2TLdFJCI+UIXjHw08GhGPS+punXHA9RGxGXhMUjvZE/QA7RGxAkDS9WldFxEzsyrZbsO6pL2BiUBLfv0+Ggp+PNmzJx3OlTQRaAMuiIingSHAPbl1VqUYwBOd4kd0dRBJk4HJAMOHD++DtM3MDIo1rM8lKyBLgIW5T69I2g34EPDvKXQ5sD/Zra41bHs7rbSImBERrRHROnDgwL7arZlZ0yvSxff1EfG5Chz7eOD+iFgL0PENIOkK4JY0uxoYlttuaIrRQ9zMzKqgyJXIzyWdKWmwpAEdnz449gRyt7IkDc4tOxlYmqbnAOMl7S5pBDASuA9YAIyUNCJd1YxP65qZWZUUuRL5G/Bt4Mu82isr6MVw8JLeSNar6lO58LckjUr7XtmxLCKWSZpN1mC+BTgnvfsdSecC84B+wMyIWFY2JzMz23FFisgFwNsj4i99ddCIeB54S6fYqT2sPxWY2kV8LlmbjZW0vffGr5x2YpUyMbN6VOR2VjvwQqUTMTOz+lPkSuR5YJGkO8mGgwf6rIuvmZnVsSJF5FfpY2Zm9hpF3icyqxqJmJlZ/SnyxPpjdDFWVkSU7p1lZmaNocjtrNbc9OuBjwB98ZyImZnVue32zoqIp3Kf1RHxfcD9Ps3MrNDtrENys7uQXZkUuYIxM7MGV6QY5AdC3EL2NPkpFcnGzMzqSpHeWdV4r4iZmdWhIrezdgf+B9u+T+TSyqVlZmb1oMjtrJuBZ8neIbJ5O+uamVkTKVJEhkbE2IpnYmZmdafIAIx/lPR3Fc/EzMzqTpErkdHAaenJ9c2AgIiI91Q0MzMz2+kVKSLHVzwLMzOrS0W6+D5ejUTMzKz+FGkTMTMz61LNioiklZKWSFokqS3FBkiaL2l5+u6f4pI0XVK7pMX5oVgkTUrrL5c0qVbnY2bWjGp9JfKBiBgVER0jBU8B7oiIkcAdaR6ydpmR6TMZuByyogNcDBwBHA5c3FF4zMys8mpdRDobB3S8BGsWcFIufnVk7gH2ljQYOA6YHxEbIuJpYD7gZ1rMzKqklkUkgNslLZQ0OcUGRcSaNP0kMChNDwGeyG27KsW6i7+GpMmS2iS1rV+/vi/PwcysqdVySPfREbFa0luB+ZIezi+MiJC0zRsVy4iIGcAMgNbW1j7Zp5mZ1fBKJCJWp+91wE1kbRpr020q0ve6tPpqYFhu86Ep1l3czMyqoCZFRNIbJe3ZMQ0cCywF5gAdPawmkQ3+SIpPTL20jgSeTbe95gHHSuqfGtSPTTEzM6uCWt3OGgTcJKkjh2sj4teSFgCzJZ0BPM6rL7+aC5wAtAMvAKcDRMQGSV8DFqT1Lo2IDdU7DTOz5laTIhIRK4D3dhF/Cji6i3gA53Szr5nAzL7O0czMts/vSrcetUy5tcflK6edWKVMzGxntLM9J2JmZnXERcTMzEpzETEzs9JcRMzMrDQXETMzK81FxMzMSnMRMTOz0lxEzMysNBcRMzMrzUXEzMxKcxExM7PSXETMzKw0FxEzMyvNRcTMzEpzETEzs9L8PhGrGL+LxKzx+UrEzMxKq3oRkTRM0p2SHpS0TNL5KX6JpNWSFqXPCbltLpLULukRScfl4mNTrF3SlGqfi5lZs6vF7awtwAURcb+kPYGFkuanZd+LiO/kV5Z0IDAeeDewL/AbSe9Ii38IHAOsAhZImhMRD1blLMzMrPpFJCLWAGvS9CZJDwFDethkHHB9RGwGHpPUDhyelrVHxAoASdendV1EzMyqpKZtIpJagIOBe1PoXEmLJc2U1D/FhgBP5DZblWLdxbs6zmRJbZLa1q9f35enYGbW1GpWRCS9CbgB+GxEbAQuB/YHRpFdqXy3r44VETMiojUiWgcOHNhXuzUza3o16eIraVeyAnJNRNwIEBFrc8uvAG5Js6uBYbnNh6YYPcTNzKwKatE7S8CVwEMRcVkuPji32snA0jQ9BxgvaXdJI4CRwH3AAmCkpBGSdiNrfJ9TjXMwM7NMLa5E/gE4FVgiaVGKfQmYIGkUEMBK4FMAEbFM0myyBvMtwDkRsRVA0rnAPKAfMDMillXvNMzMrBa9s34PqItFc3vYZiowtYv43J62s51bT0+0+2l2s/rgJ9bNzKw0FxEzMyvNRcTMzEpzETEzs9JcRMzMrDQXETMzK81FxMzMSnMRMTOz0vx6XKtLfvWu2c7BVyJmZlaai4iZmZXmImJmZqW5iJiZWWluWLeG5BGCzarDVyJmZlaai4iZmZXm21lmnfhWmFlxvhIxM7PS6v5KRNJY4F/J3rP+04iYVuOUrIH5SXmz16rrIiKpH/BD4BhgFbBA0pyIeLC2mZl1zbfKrNHUdREBDgfaI2IFgKTrgXGAi4jVnd5c5Wxv2+3pzb5d/JqbIqLWOZQm6cPA2Ij4ZJo/FTgiIs7ttN5kYHKaPQB4JLd4H+AvVUi3Vnx+9a/Rz7HRzw8a4xzfFhEDOwfr/UqkkIiYAczoapmktohorXJKVePzq3+Nfo6Nfn7Q2OdY772zVgPDcvNDU8zMzKqg3ovIAmCkpBGSdgPGA3NqnJOZWdOo69tZEbFF0rnAPLIuvjMjYtkO7qbL21wNxOdX/xr9HBv9/KCBz7GuG9bNzKy26v12lpmZ1ZCLiJmZlda0RUTSWEmPSGqXNKXW+VSCpJWSlkhaJKmt1vn0lqSZktZJWpqLDZA0X9Ly9N2/ljn2VjfneImk1el3XCTphFrm2BuShkm6U9KDkpZJOj/FG+J37OH8GuY37Kwp20TScCl/JjdcCjCh0YZLkbQSaI2Ien/ICQBJRwHPAVdHxEEp9i1gQ0RMS/8z0D8iLqxlnr3RzTleAjwXEd+pZW59QdJgYHBE3C9pT2AhcBJwGg3wO/ZwfqfQIL9hZ816JfLKcCkR8TegY7gU24lFxN3Ahk7hccCsND2L7B9s3ermHBtGRKyJiPvT9CbgIWAIDfI79nB+DatZi8gQ4Inc/Coa84cO4HZJC9PQL41oUESsSdNPAoNqmUwFnStpcbrdVZe3ejqT1AIcDNxLA/6Onc4PGvA3hOYtIs1idEQcAhwPnJNulTSsyO7NNuL92cuB/YFRwBrguzXNpg9IehNwA/DZiNiYX9YIv2MX59dwv2GHZi0iTTFcSkSsTt/rgJvIbuM1mrXpPnTH/eh1Nc6nz0XE2ojYGhEvA1dQ57+jpF3J/sBeExE3pnDD/I5dnV+j/YZ5zVpEGn64FElvTA17SHojcCywtOet6tIcYFKangTcXMNcKqLjj2tyMnX8O0oScCXwUERcllvUEL9jd+fXSL9hZ03ZOwsgdbH7Pq8OlzK1thn1LUn7kV19QDa8zbX1fo6SrgPGkA2rvRa4GPgVMBsYDjwOnBIRddsw3c05jiG7DRLASuBTufaDuiJpNPA7YAnwcgp/iazdoO5/xx7ObwIN8ht21rRFxMzMeq9Zb2eZmVkfcBExM7PSXETMzKw0FxEzMyvNRcTMzEpzEbGGJum5CuxzVH4U1jRC6+d7sb+PSHpI0p19k2HpPFZK2qeWOVj9cREx23GjgL4cyvsM4MyI+EAf7tOsKlxErGlI+oKkBWkQvH9JsZZ0FXBFev/D7ZL2SMsOS+sukvRtSUvTCAeXAh9N8Y+m3R8o6S5JKySd183xJ6T3uyyV9M0U+yowGrhS0rc7rT9Y0t3pOEslvS/FL5fUlvL9l9z6KyV9I63fJukQSfMkPSrprLTOmLTPW5W9T+fHkrb5OyDp45LuS/v6iaR+6XNVymWJpP/Vy5/EGkFE+ONPw37I3uEA2bAvMwCR/c/TLcBRQAuwBRiV1psNfDxNLwX+Pk1PA5am6dOAH+SOcQnwR2B3sifNnwJ27ZTHvsD/AwaSjSDwf4CT0rK7yN770jn3C4Avp+l+wJ5pekAudhfwnjS/Evh0mv4esBjYMx1zbYqPAf4K7Je2nw98OLf9PsC7gP/sOAfgR8BE4FBgfi6/vWv9+/pT+4+vRKxZHJs+DwD3A+8ERqZlj0XEojS9EGiRtDfZH+0/pfi129n/rRGxObIXgK1j26HMDwPuioj1EbEFuIasiPVkAXB6einV30X2fgqAUyTdn87l3cCBuW06xoBbAtwbEZsiYj2wOZ0TwH2RvUtnK3Ad2ZVQ3tFkBWOBpEVpfj9gBbCfpH+TNBbYiDW919U6AbMqEfCNiPjJa4LZOx8250JbgT1K7L/zPnr9bysi7k7D958IXCXpMrJxmT4PHBYRT0u6Cnh9F3m83Cmnl3M5dR7rqPO8gFkRcVHnnCS9FzgOOIvsbX2f2NHzssbiKxFrFvOAT6T3PCBpiKS3drdyRDwDbJJ0RAqNzy3eRHabaEfcB7xf0j7KXs88AfhtTxtIehvZbagrgJ8ChwB7Ac8Dz0oaRPaumB11eBrBehfgo8DvOy2/A/hwx38fZe8/f1vqubVLRNwAfCXlY03OVyLWFCLidknvAv6UjdbNc8DHya4aunMGcIWkl8n+4D+b4ncCU9Ktnm8UPP4aZe8Ov5Ps//RvjYjtDXc+BviCpJdSvhMj4jFJDwAPk72d8w9Fjt/JAuAHwNtTPjflF0bEg5K+QvZWzF2Al4BzgBeBn+Ua4re5UrHm41F8zboh6U0R8VyangIMjojza5xWr0gaA3w+Ij5Y41SsQfhKxKx7J0q6iOzfyeNkvbLMLMdXImZmVpob1s3MrDQXETMzK81FxMzMSnMRMTOz0lxEzMystP8PpPFMfpeALD4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 길이 분포 출력\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "text_len = [len(s.split()) for s in data['Text']]\n",
    "summary_len = [len(s.split()) for s in data['Summary']]\n",
    "\n",
    "print('텍스트의 최소 길이 : {}'.format(np.min(text_len)))\n",
    "print('텍스트의 최대 길이 : {}'.format(np.max(text_len)))\n",
    "print('텍스트의 평균 길이 : {}'.format(np.mean(text_len)))\n",
    "print('요약의 최소 길이 : {}'.format(np.min(summary_len)))\n",
    "print('요약의 최대 길이 : {}'.format(np.max(summary_len)))\n",
    "print('요약의 평균 길이 : {}'.format(np.mean(summary_len)))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.boxplot(text_len)\n",
    "plt.title('Text')\n",
    "plt.subplot(1,2,2)\n",
    "plt.boxplot(summary_len)\n",
    "plt.title('Summary')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "plt.title('Text')\n",
    "plt.hist(text_len, bins = 40)\n",
    "plt.xlabel('length of samples')\n",
    "plt.ylabel('number of samples')\n",
    "plt.show()\n",
    "\n",
    "plt.title('Summary')\n",
    "plt.hist(summary_len, bins = 40)\n",
    "plt.xlabel('length of samples')\n",
    "plt.ylabel('number of samples')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "involved-platform",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=3\n"
     ]
    }
   ],
   "source": [
    "# 임의로 정한 max_len\n",
    "text_max_len = 50\n",
    "summary_max_len = 8\n",
    "print('=3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "discrete-alert",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=3\n"
     ]
    }
   ],
   "source": [
    "def below_threshold_len(max_len, nested_list):\n",
    "    cnt = 0\n",
    "    for s in nested_list:\n",
    "        if(len(s.split()) <= max_len):\n",
    "            cnt = cnt + 1\n",
    "    print('전체 샘플 중 길이가 %s 이하인 샘플의 비율: %s'%(max_len, (cnt / len(nested_list))))\n",
    "print('=3')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fantastic-relation",
   "metadata": {},
   "source": [
    "--------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "delayed-coaching",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "텍스트의 최소 길이 : 2\n",
      "텍스트의 최대 길이 : 1235\n",
      "텍스트의 평균 길이 : 38.792428272310566\n",
      "요약의 최소 길이 : 1\n",
      "요약의 최대 길이 : 28\n",
      "요약의 평균 길이 : 4.010729443721352\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # 길이 분포 출력\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "# text_len = [len(s.split()) for s in data['Text']]\n",
    "# summary_len = [len(s.split()) for s in data['Summary']]\n",
    "\n",
    "# print('텍스트의 최소 길이 : {}'.format(np.min(text_len)))\n",
    "# print('텍스트의 최대 길이 : {}'.format(np.max(text_len)))\n",
    "# print('텍스트의 평균 길이 : {}'.format(np.mean(text_len)))\n",
    "# print('요약의 최소 길이 : {}'.format(np.min(summary_len)))\n",
    "# print('요약의 최대 길이 : {}'.format(np.max(summary_len)))\n",
    "# print('요약의 평균 길이 : {}'.format(np.mean(summary_len)))\n",
    "\n",
    "# plt.subplot(1,2,1)\n",
    "# plt.boxplot(text_len)\n",
    "# plt.title('Text')\n",
    "# plt.subplot(1,2,2)\n",
    "# plt.boxplot(summary_len)\n",
    "# plt.title('Summary')\n",
    "# # plt.tight_layout() # tight_layout() 안 하면 layout 안에 꽉 들어차게 들어가지 않는다.\n",
    "# plt.show()\n",
    "\n",
    "# plt.title('Text')\n",
    "# plt.hist(text_len, bins = 40)\n",
    "# plt.xlabel('length of samples')\n",
    "# plt.ylabel('number of samples')\n",
    "# plt.show()\n",
    "\n",
    "# plt.title('Summary')\n",
    "# plt.hist(summary_len, bins = 40)\n",
    "# plt.xlabel('length of samples')\n",
    "# plt.ylabel('number of samples')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "noted-representation",
   "metadata": {},
   "source": [
    "--------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "romance-vegetarian",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "전체 샘플 중 길이가 50 이하인 샘플의 비율: 0.7745119121724859\n",
      "전체 샘플 중 길이가 8 이하인 샘플의 비율: 0.9424593967517402\n"
     ]
    }
   ],
   "source": [
    "below_threshold_len(text_max_len, data['Text'])\n",
    "below_threshold_len(summary_max_len,  data['Summary'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "corrected-cancer",
   "metadata": {},
   "source": [
    "> max_len을 정했으면 모든 데이터를 그것에 맞춰서 자를 건지, 아님 데이터 중 그것에 맞는 것만 가질 건지 정하면 되는데,  \n",
    "> 내용이 잘리면 문맥상 이상해질 수 있으니 여기서는 제외한다고 함(약 23% 제외될 예정)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "starting-freight",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "전체 샘플수 : 65818\n"
     ]
    }
   ],
   "source": [
    "data = data[data['Text'].apply(lambda x: len(x.split()) <= text_max_len)]\n",
    "data = data[data['Summary'].apply(lambda x: len(x.split()) <= summary_max_len)]\n",
    "print('전체 샘플수 :', (len(data)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "institutional-payroll",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        True\n",
       "1        True\n",
       "2        True\n",
       "3        True\n",
       "4        True\n",
       "         ... \n",
       "99993    True\n",
       "99994    True\n",
       "99995    True\n",
       "99997    True\n",
       "99998    True\n",
       "Name: Text, Length: 65818, dtype: bool"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DJ\n",
    "# 이런 식으로 lambda 뒤에 반환 값으로 boolean 값을 넣을 수도 있음\n",
    "data['Text'].apply(lambda x: len(x.split()) <= text_max_len)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "consolidated-rings",
   "metadata": {},
   "source": [
    "#### 시작 토큰과 종료 토큰 추가하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "sound-appreciation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text</th>\n",
       "      <th>Summary</th>\n",
       "      <th>decoder_input</th>\n",
       "      <th>decoder_target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bought several vitality canned dog food produc...</td>\n",
       "      <td>good quality dog food</td>\n",
       "      <td>sostoken good quality dog food</td>\n",
       "      <td>good quality dog food eostoken</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>product arrived labeled jumbo salted peanuts p...</td>\n",
       "      <td>not as advertised</td>\n",
       "      <td>sostoken not as advertised</td>\n",
       "      <td>not as advertised eostoken</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>confection around centuries light pillowy citr...</td>\n",
       "      <td>delight says it all</td>\n",
       "      <td>sostoken delight says it all</td>\n",
       "      <td>delight says it all eostoken</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>looking secret ingredient robitussin believe f...</td>\n",
       "      <td>cough medicine</td>\n",
       "      <td>sostoken cough medicine</td>\n",
       "      <td>cough medicine eostoken</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>great taffy great price wide assortment yummy ...</td>\n",
       "      <td>great taffy</td>\n",
       "      <td>sostoken great taffy</td>\n",
       "      <td>great taffy eostoken</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Text                Summary  \\\n",
       "0  bought several vitality canned dog food produc...  good quality dog food   \n",
       "1  product arrived labeled jumbo salted peanuts p...      not as advertised   \n",
       "2  confection around centuries light pillowy citr...    delight says it all   \n",
       "3  looking secret ingredient robitussin believe f...         cough medicine   \n",
       "4  great taffy great price wide assortment yummy ...            great taffy   \n",
       "\n",
       "                    decoder_input                  decoder_target  \n",
       "0  sostoken good quality dog food  good quality dog food eostoken  \n",
       "1      sostoken not as advertised      not as advertised eostoken  \n",
       "2    sostoken delight says it all    delight says it all eostoken  \n",
       "3         sostoken cough medicine         cough medicine eostoken  \n",
       "4            sostoken great taffy            great taffy eostoken  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 요약 데이터에는 시작 토큰과 종료 토큰을 추가한다.\n",
    "data['decoder_input'] = data['Summary'].apply(lambda x : 'sostoken '+ x)\n",
    "data['decoder_target'] = data['Summary'].apply(lambda x : x + ' eostoken')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "personalized-finnish",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=3\n"
     ]
    }
   ],
   "source": [
    "# 필요한 자료 넘파이 어레이로 저장하기\n",
    "encoder_input = np.array(data['Text']) # 인코더의 입력\n",
    "decoder_input = np.array(data['decoder_input']) # 디코더의 입력\n",
    "decoder_target = np.array(data['decoder_target']) # 디코더의 레이블\n",
    "print('=3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "norwegian-tiffany",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['bought several vitality canned dog food products found good quality product looks like stew processed meat smells better labrador finicky appreciates product better',\n",
       "       'product arrived labeled jumbo salted peanuts peanuts actually small sized unsalted sure error vendor intended represent product jumbo'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder_input[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "resistant-colorado",
   "metadata": {},
   "source": [
    "#### train, test 나누기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "bibliographic-revelation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21581 14204 40943 ... 50248 22198  2096]\n"
     ]
    }
   ],
   "source": [
    "# 일단 인풋 데이터 갯수와 맞는 indices 만들고 섞기\n",
    "indices = np.arange(encoder_input.shape[0])\n",
    "np.random.shuffle(indices)\n",
    "print(indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "introductory-multiple",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([52954, 55218, 52351, ..., 63761, 52490, 55588])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# permutation은 반환하고,\n",
    "# shuffle은 called 된 것을 shuffle해줌\n",
    "np.random.permutation(np.arange(len(encoder_input)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "attached-spotlight",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=3\n"
     ]
    }
   ],
   "source": [
    "# 섞어주기\n",
    "encoder_input = encoder_input[indices]\n",
    "decoder_input = decoder_input[indices]\n",
    "decoder_target = decoder_target[indices]\n",
    "print('=3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "published-poker",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "테스트 데이터의 수 : 13163\n"
     ]
    }
   ],
   "source": [
    "# 8:2로 나눠주기\n",
    "n_of_val = int(len(encoder_input)*0.2)\n",
    "print('테스트 데이터의 수 :', n_of_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "comparable-relay",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65818,)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder_input.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "least-recall",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 데이터의 개수 : 52655\n",
      "훈련 레이블의 개수 : 52655\n",
      "테스트 데이터의 개수 : 13163\n",
      "테스트 레이블의 개수 : 13163\n"
     ]
    }
   ],
   "source": [
    "encoder_input_train = encoder_input[:-n_of_val]\n",
    "decoder_input_train = decoder_input[:-n_of_val]\n",
    "decoder_target_train = decoder_target[:-n_of_val]\n",
    "\n",
    "encoder_input_test = encoder_input[-n_of_val:]\n",
    "decoder_input_test = decoder_input[-n_of_val:]\n",
    "decoder_target_test = decoder_target[-n_of_val:]\n",
    "\n",
    "print('훈련 데이터의 개수 :', len(encoder_input_train))\n",
    "print('훈련 레이블의 개수 :', len(decoder_input_train))\n",
    "print('테스트 데이터의 개수 :', len(encoder_input_test))\n",
    "print('테스트 레이블의 개수 :', len(decoder_input_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "excess-missouri",
   "metadata": {},
   "source": [
    "### (3) 정수 인코딩"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "false-floating",
   "metadata": {},
   "source": [
    "#### 단어 집합(vocabulary) 만들기 및 정수 인코딩\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "handled-recall",
   "metadata": {},
   "source": [
    "> keras의 `Tokenizer`를 사용하면 손 쉽게 단어 집합을 만들 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "strong-lodging",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=3\n"
     ]
    }
   ],
   "source": [
    "src_tokenizer = Tokenizer() # 토크나이저 정의\n",
    "src_tokenizer.fit_on_texts(encoder_input_train) # 입력된 데이터로부터 단어 집합 생성\n",
    "print('=3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "executive-transaction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras_preprocessing.text.Tokenizer at 0x7fa7c47fb310>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tokenizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "documentary-sleep",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras_preprocessing.text.Tokenizer at 0x7fa7c4810a10>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "src_tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "hidden-accreditation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'like': 1,\n",
       " 'good': 2,\n",
       " 'great': 3,\n",
       " 'taste': 4,\n",
       " 'product': 5,\n",
       " 'love': 6,\n",
       " 'one': 7,\n",
       " 'coffee': 8,\n",
       " 'flavor': 9,\n",
       " 'would': 10,\n",
       " 'tea': 11,\n",
       " 'really': 12,\n",
       " 'get': 13,\n",
       " 'amazon': 14,\n",
       " 'price': 15,\n",
       " 'best': 16,\n",
       " 'buy': 17,\n",
       " 'much': 18,\n",
       " 'food': 19,\n",
       " 'time': 20,\n",
       " 'use': 21,\n",
       " 'little': 22,\n",
       " 'find': 23,\n",
       " 'tried': 24,\n",
       " 'better': 25,\n",
       " 'also': 26,\n",
       " 'well': 27,\n",
       " 'make': 28,\n",
       " 'chocolate': 29,\n",
       " 'try': 30,\n",
       " 'eat': 31,\n",
       " 'dog': 32,\n",
       " 'even': 33,\n",
       " 'bought': 34,\n",
       " 'delicious': 35,\n",
       " 'found': 36,\n",
       " 'sweet': 37,\n",
       " 'order': 38,\n",
       " 'could': 39,\n",
       " 'cup': 40,\n",
       " 'recommend': 41,\n",
       " 'drink': 42,\n",
       " 'tastes': 43,\n",
       " 'used': 44,\n",
       " 'loves': 45,\n",
       " 'bag': 46,\n",
       " 'sugar': 47,\n",
       " 'favorite': 48,\n",
       " 'cannot': 49,\n",
       " 'first': 50,\n",
       " 'store': 51,\n",
       " 'free': 52,\n",
       " 'nice': 53,\n",
       " 'made': 54,\n",
       " 'way': 55,\n",
       " 'box': 56,\n",
       " 'go': 57,\n",
       " 'got': 58,\n",
       " 'perfect': 59,\n",
       " 'mix': 60,\n",
       " 'dogs': 61,\n",
       " 'water': 62,\n",
       " 'since': 63,\n",
       " 'easy': 64,\n",
       " 'day': 65,\n",
       " 'think': 66,\n",
       " 'ordered': 67,\n",
       " 'snack': 68,\n",
       " 'bit': 69,\n",
       " 'give': 70,\n",
       " 'hot': 71,\n",
       " 'flavors': 72,\n",
       " 'two': 73,\n",
       " 'many': 74,\n",
       " 'still': 75,\n",
       " 'never': 76,\n",
       " 'ever': 77,\n",
       " 'makes': 78,\n",
       " 'every': 79,\n",
       " 'stuff': 80,\n",
       " 'treats': 81,\n",
       " 'quality': 82,\n",
       " 'always': 83,\n",
       " 'without': 84,\n",
       " 'brand': 85,\n",
       " 'right': 86,\n",
       " 'know': 87,\n",
       " 'healthy': 88,\n",
       " 'fresh': 89,\n",
       " 'add': 90,\n",
       " 'tasty': 91,\n",
       " 'definitely': 92,\n",
       " 'hard': 93,\n",
       " 'want': 94,\n",
       " 'lot': 95,\n",
       " 'years': 96,\n",
       " 'keep': 97,\n",
       " 'something': 98,\n",
       " 'strong': 99,\n",
       " 'buying': 100,\n",
       " 'happy': 101,\n",
       " 'small': 102,\n",
       " 'enjoy': 103,\n",
       " 'wonderful': 104,\n",
       " 'treat': 105,\n",
       " 'enough': 106,\n",
       " 'bad': 107,\n",
       " 'excellent': 108,\n",
       " 'local': 109,\n",
       " 'old': 110,\n",
       " 'chips': 111,\n",
       " 'less': 112,\n",
       " 'milk': 113,\n",
       " 'say': 114,\n",
       " 'different': 115,\n",
       " 'highly': 116,\n",
       " 'purchase': 117,\n",
       " 'shipping': 118,\n",
       " 'need': 119,\n",
       " 'however': 120,\n",
       " 'organic': 121,\n",
       " 'sure': 122,\n",
       " 'regular': 123,\n",
       " 'far': 124,\n",
       " 'size': 125,\n",
       " 'thought': 126,\n",
       " 'eating': 127,\n",
       " 'salt': 128,\n",
       " 'tasting': 129,\n",
       " 'long': 130,\n",
       " 'products': 131,\n",
       " 'tasted': 132,\n",
       " 'stores': 133,\n",
       " 'put': 134,\n",
       " 'though': 135,\n",
       " 'using': 136,\n",
       " 'looking': 137,\n",
       " 'back': 138,\n",
       " 'purchased': 139,\n",
       " 'whole': 140,\n",
       " 'pack': 141,\n",
       " 'bars': 142,\n",
       " 'pretty': 143,\n",
       " 'cups': 144,\n",
       " 'bags': 145,\n",
       " 'loved': 146,\n",
       " 'work': 147,\n",
       " 'worth': 148,\n",
       " 'high': 149,\n",
       " 'gluten': 150,\n",
       " 'low': 151,\n",
       " 'cookies': 152,\n",
       " 'texture': 153,\n",
       " 'package': 154,\n",
       " 'family': 155,\n",
       " 'big': 156,\n",
       " 'ingredients': 157,\n",
       " 'sauce': 158,\n",
       " 'arrived': 159,\n",
       " 'thing': 160,\n",
       " 'received': 161,\n",
       " 'quite': 162,\n",
       " 'green': 163,\n",
       " 'grocery': 164,\n",
       " 'last': 165,\n",
       " 'popcorn': 166,\n",
       " 'rice': 167,\n",
       " 'going': 168,\n",
       " 'candy': 169,\n",
       " 'year': 170,\n",
       " 'take': 171,\n",
       " 'item': 172,\n",
       " 'oil': 173,\n",
       " 'real': 174,\n",
       " 'actually': 175,\n",
       " 'came': 176,\n",
       " 'expensive': 177,\n",
       " 'cat': 178,\n",
       " 'anything': 179,\n",
       " 'almost': 180,\n",
       " 'calories': 181,\n",
       " 'dark': 182,\n",
       " 'cereal': 183,\n",
       " 'see': 184,\n",
       " 'chicken': 185,\n",
       " 'husband': 186,\n",
       " 'feel': 187,\n",
       " 'natural': 188,\n",
       " 'new': 189,\n",
       " 'butter': 190,\n",
       " 'money': 191,\n",
       " 'another': 192,\n",
       " 'full': 193,\n",
       " 'kids': 194,\n",
       " 'trying': 195,\n",
       " 'blend': 196,\n",
       " 'people': 197,\n",
       " 'bar': 198,\n",
       " 'brands': 199,\n",
       " 'getting': 200,\n",
       " 'fruit': 201,\n",
       " 'smell': 202,\n",
       " 'morning': 203,\n",
       " 'absolutely': 204,\n",
       " 'coconut': 205,\n",
       " 'disappointed': 206,\n",
       " 'home': 207,\n",
       " 'several': 208,\n",
       " 'amount': 209,\n",
       " 'fast': 210,\n",
       " 'added': 211,\n",
       " 'around': 212,\n",
       " 'half': 213,\n",
       " 'away': 214,\n",
       " 'wish': 215,\n",
       " 'cheese': 216,\n",
       " 'diet': 217,\n",
       " 'available': 218,\n",
       " 'bitter': 219,\n",
       " 'oz': 220,\n",
       " 'us': 221,\n",
       " 'deal': 222,\n",
       " 'kind': 223,\n",
       " 'able': 224,\n",
       " 'dry': 225,\n",
       " 'fat': 226,\n",
       " 'usually': 227,\n",
       " 'foods': 228,\n",
       " 'ones': 229,\n",
       " 'cats': 230,\n",
       " 'flavored': 231,\n",
       " 'probably': 232,\n",
       " 'smooth': 233,\n",
       " 'nothing': 234,\n",
       " 'come': 235,\n",
       " 'may': 236,\n",
       " 'case': 237,\n",
       " 'works': 238,\n",
       " 'light': 239,\n",
       " 'liked': 240,\n",
       " 'per': 241,\n",
       " 'ordering': 242,\n",
       " 'days': 243,\n",
       " 'company': 244,\n",
       " 'said': 245,\n",
       " 'breakfast': 246,\n",
       " 'problem': 247,\n",
       " 'gift': 248,\n",
       " 'bread': 249,\n",
       " 'cheaper': 250,\n",
       " 'save': 251,\n",
       " 'quick': 252,\n",
       " 'things': 253,\n",
       " 'boxes': 254,\n",
       " 'months': 255,\n",
       " 'seems': 256,\n",
       " 'making': 257,\n",
       " 'quickly': 258,\n",
       " 'variety': 259,\n",
       " 'black': 260,\n",
       " 'fine': 261,\n",
       " 'especially': 262,\n",
       " 'large': 263,\n",
       " 'peanut': 264,\n",
       " 'packaging': 265,\n",
       " 'reviews': 266,\n",
       " 'pasta': 267,\n",
       " 'glad': 268,\n",
       " 'bottle': 269,\n",
       " 'rich': 270,\n",
       " 'others': 271,\n",
       " 'honey': 272,\n",
       " 'instead': 273,\n",
       " 'son': 274,\n",
       " 'three': 275,\n",
       " 'recommended': 276,\n",
       " 'juice': 277,\n",
       " 'house': 278,\n",
       " 'gave': 279,\n",
       " 'comes': 280,\n",
       " 'drinking': 281,\n",
       " 'protein': 282,\n",
       " 'top': 283,\n",
       " 'roast': 284,\n",
       " 'prefer': 285,\n",
       " 'energy': 286,\n",
       " 'thank': 287,\n",
       " 'spicy': 288,\n",
       " 'look': 289,\n",
       " 'syrup': 290,\n",
       " 'amazing': 291,\n",
       " 'anyone': 292,\n",
       " 'times': 293,\n",
       " 'everyone': 294,\n",
       " 'must': 295,\n",
       " 'month': 296,\n",
       " 'thanks': 297,\n",
       " 'extra': 298,\n",
       " 'cans': 299,\n",
       " 'meal': 300,\n",
       " 'cream': 301,\n",
       " 'white': 302,\n",
       " 'yummy': 303,\n",
       " 'next': 304,\n",
       " 'fan': 305,\n",
       " 'daughter': 306,\n",
       " 'maybe': 307,\n",
       " 'teeth': 308,\n",
       " 'pieces': 309,\n",
       " 'seem': 310,\n",
       " 'vanilla': 311,\n",
       " 'value': 312,\n",
       " 'awesome': 313,\n",
       " 'wanted': 314,\n",
       " 'fact': 315,\n",
       " 'likes': 316,\n",
       " 'might': 317,\n",
       " 'soft': 318,\n",
       " 'everything': 319,\n",
       " 'salty': 320,\n",
       " 'keurig': 321,\n",
       " 'stars': 322,\n",
       " 'health': 323,\n",
       " 'either': 324,\n",
       " 'else': 325,\n",
       " 'friends': 326,\n",
       " 'nuts': 327,\n",
       " 'teas': 328,\n",
       " 'crunchy': 329,\n",
       " 'cost': 330,\n",
       " 'plus': 331,\n",
       " 'least': 332,\n",
       " 'longer': 333,\n",
       " 'baby': 334,\n",
       " 'enjoyed': 335,\n",
       " 'super': 336,\n",
       " 'yet': 337,\n",
       " 'bold': 338,\n",
       " 'couple': 339,\n",
       " 'ginger': 340,\n",
       " 'went': 341,\n",
       " 'online': 342,\n",
       " 'beans': 343,\n",
       " 'ago': 344,\n",
       " 'open': 345,\n",
       " 'soup': 346,\n",
       " 'market': 347,\n",
       " 'pleased': 348,\n",
       " 'plastic': 349,\n",
       " 'decided': 350,\n",
       " 'ice': 351,\n",
       " 'took': 352,\n",
       " 'ok': 353,\n",
       " 'chew': 354,\n",
       " 'help': 355,\n",
       " 'started': 356,\n",
       " 'corn': 357,\n",
       " 'expected': 358,\n",
       " 'alternative': 359,\n",
       " 'noodles': 360,\n",
       " 'flavorful': 361,\n",
       " 'delivery': 362,\n",
       " 'although': 363,\n",
       " 'snacks': 364,\n",
       " 'says': 365,\n",
       " 'week': 366,\n",
       " 'mouth': 367,\n",
       " 'rather': 368,\n",
       " 'machine': 369,\n",
       " 'jerky': 370,\n",
       " 'minutes': 371,\n",
       " 'service': 372,\n",
       " 'pepper': 373,\n",
       " 'wheat': 374,\n",
       " 'ounce': 375,\n",
       " 'cocoa': 376,\n",
       " 'cold': 377,\n",
       " 'goes': 378,\n",
       " 'crackers': 379,\n",
       " 'read': 380,\n",
       " 'review': 381,\n",
       " 'let': 382,\n",
       " 'beef': 383,\n",
       " 'lemon': 384,\n",
       " 'second': 385,\n",
       " 'plain': 386,\n",
       " 'powder': 387,\n",
       " 'cook': 388,\n",
       " 'drinks': 389,\n",
       " 'soda': 390,\n",
       " 'type': 391,\n",
       " 'cookie': 392,\n",
       " 'weight': 393,\n",
       " 'opened': 394,\n",
       " 'decaf': 395,\n",
       " 'special': 396,\n",
       " 'gets': 397,\n",
       " 'surprised': 398,\n",
       " 'meat': 399,\n",
       " 'sometimes': 400,\n",
       " 'wife': 401,\n",
       " 'version': 402,\n",
       " 'cooking': 403,\n",
       " 'french': 404,\n",
       " 'shipped': 405,\n",
       " 'aroma': 406,\n",
       " 'red': 407,\n",
       " 'exactly': 408,\n",
       " 'dried': 409,\n",
       " 'part': 410,\n",
       " 'convenient': 411,\n",
       " 'fantastic': 412,\n",
       " 'friend': 413,\n",
       " 'smaller': 414,\n",
       " 'tell': 415,\n",
       " 'place': 416,\n",
       " 'cake': 417,\n",
       " 'clean': 418,\n",
       " 'picky': 419,\n",
       " 'choice': 420,\n",
       " 'hand': 421,\n",
       " 'easily': 422,\n",
       " 'side': 423,\n",
       " 'mixed': 424,\n",
       " 'believe': 425,\n",
       " 'packaged': 426,\n",
       " 'wrong': 427,\n",
       " 'reason': 428,\n",
       " 'smells': 429,\n",
       " 'problems': 430,\n",
       " 'orange': 431,\n",
       " 'artificial': 432,\n",
       " 'guess': 433,\n",
       " 'difference': 434,\n",
       " 'refreshing': 435,\n",
       " 'giving': 436,\n",
       " 'care': 437,\n",
       " 'subscribe': 438,\n",
       " 'coffees': 439,\n",
       " 'slightly': 440,\n",
       " 'bulk': 441,\n",
       " 'packs': 442,\n",
       " 'live': 443,\n",
       " 'left': 444,\n",
       " 'seeds': 445,\n",
       " 'aftertaste': 446,\n",
       " 'continue': 447,\n",
       " 'cinnamon': 448,\n",
       " 'serving': 449,\n",
       " 'recently': 450,\n",
       " 'christmas': 451,\n",
       " 'pop': 452,\n",
       " 'container': 453,\n",
       " 'chewy': 454,\n",
       " 'cut': 455,\n",
       " 'mild': 456,\n",
       " 'starbucks': 457,\n",
       " 'recipe': 458,\n",
       " 'etc': 459,\n",
       " 'spice': 460,\n",
       " 'bite': 461,\n",
       " 'potato': 462,\n",
       " 'oatmeal': 463,\n",
       " 'delivered': 464,\n",
       " 'line': 465,\n",
       " 'brown': 466,\n",
       " 'often': 467,\n",
       " 'sent': 468,\n",
       " 'brew': 469,\n",
       " 'stomach': 470,\n",
       " 'lots': 471,\n",
       " 'almonds': 472,\n",
       " 'iced': 473,\n",
       " 'needed': 474,\n",
       " 'looked': 475,\n",
       " 'bottles': 476,\n",
       " 'dinner': 477,\n",
       " 'pay': 478,\n",
       " 'looks': 479,\n",
       " 'compared': 480,\n",
       " 'huge': 481,\n",
       " 'fiber': 482,\n",
       " 'cherry': 483,\n",
       " 'past': 484,\n",
       " 'apple': 485,\n",
       " 'run': 486,\n",
       " 'ate': 487,\n",
       " 'calorie': 488,\n",
       " 'filling': 489,\n",
       " 'lunch': 490,\n",
       " 'life': 491,\n",
       " 'night': 492,\n",
       " 'end': 493,\n",
       " 'simply': 494,\n",
       " 'excited': 495,\n",
       " 'date': 496,\n",
       " 'close': 497,\n",
       " 'packages': 498,\n",
       " 'gives': 499,\n",
       " 'carry': 500,\n",
       " 'seller': 501,\n",
       " 'larger': 502,\n",
       " 'start': 503,\n",
       " 'granola': 504,\n",
       " 'chai': 505,\n",
       " 'weeks': 506,\n",
       " 'seasoning': 507,\n",
       " 'eaten': 508,\n",
       " 'stick': 509,\n",
       " 'original': 510,\n",
       " 'toy': 511,\n",
       " 'healthier': 512,\n",
       " 'items': 513,\n",
       " 'com': 514,\n",
       " 'unfortunately': 515,\n",
       " 'chip': 516,\n",
       " 'someone': 517,\n",
       " 'waste': 518,\n",
       " 'needs': 519,\n",
       " 'salad': 520,\n",
       " 'creamy': 521,\n",
       " 'helps': 522,\n",
       " 'saw': 523,\n",
       " 'condition': 524,\n",
       " 'hope': 525,\n",
       " 'soon': 526,\n",
       " 'similar': 527,\n",
       " 'canned': 528,\n",
       " 'broken': 529,\n",
       " 'gum': 530,\n",
       " 'color': 531,\n",
       " 'olive': 532,\n",
       " 'nut': 533,\n",
       " 'gone': 534,\n",
       " 'inside': 535,\n",
       " 'already': 536,\n",
       " 'purchasing': 537,\n",
       " 'overall': 538,\n",
       " 'feed': 539,\n",
       " 'extremely': 540,\n",
       " 'takes': 541,\n",
       " 'jar': 542,\n",
       " 'stock': 543,\n",
       " 'stale': 544,\n",
       " 'idea': 545,\n",
       " 'sweetness': 546,\n",
       " 'four': 547,\n",
       " 'crunch': 548,\n",
       " 'heat': 549,\n",
       " 'star': 550,\n",
       " 'pound': 551,\n",
       " 'weak': 552,\n",
       " 'packed': 553,\n",
       " 'wait': 554,\n",
       " 'finally': 555,\n",
       " 'single': 556,\n",
       " 'pumpkin': 557,\n",
       " 'keeps': 558,\n",
       " 'beat': 559,\n",
       " 'mine': 560,\n",
       " 'microwave': 561,\n",
       " 'baking': 562,\n",
       " 'soy': 563,\n",
       " 'fish': 564,\n",
       " 'change': 565,\n",
       " 'caffeine': 566,\n",
       " 'experience': 567,\n",
       " 'consistency': 568,\n",
       " 'course': 569,\n",
       " 'sold': 570,\n",
       " 'non': 571,\n",
       " 'along': 572,\n",
       " 'expect': 573,\n",
       " 'medium': 574,\n",
       " 'sour': 575,\n",
       " 'hooked': 576,\n",
       " 'given': 577,\n",
       " 'daily': 578,\n",
       " 'shipment': 579,\n",
       " 'espresso': 580,\n",
       " 'puppy': 581,\n",
       " 'hours': 582,\n",
       " 'certainly': 583,\n",
       " 'please': 584,\n",
       " 'difficult': 585,\n",
       " 'ground': 586,\n",
       " 'totally': 587,\n",
       " 'instant': 588,\n",
       " 'anywhere': 589,\n",
       " 'reasonable': 590,\n",
       " 'hit': 591,\n",
       " 'leave': 592,\n",
       " 'flour': 593,\n",
       " 'maker': 594,\n",
       " 'greenies': 595,\n",
       " 'rest': 596,\n",
       " 'locally': 597,\n",
       " 'within': 598,\n",
       " 'training': 599,\n",
       " 'due': 600,\n",
       " 'packets': 601,\n",
       " 'name': 602,\n",
       " 'stop': 603,\n",
       " 'sodium': 604,\n",
       " 'list': 605,\n",
       " 'adding': 606,\n",
       " 'lb': 607,\n",
       " 'sale': 608,\n",
       " 'licorice': 609,\n",
       " 'moist': 610,\n",
       " 'eats': 611,\n",
       " 'banana': 612,\n",
       " 'pork': 613,\n",
       " 'stopped': 614,\n",
       " 'cheap': 615,\n",
       " 'together': 616,\n",
       " 'anymore': 617,\n",
       " 'hair': 618,\n",
       " 'break': 619,\n",
       " 'easier': 620,\n",
       " 'based': 621,\n",
       " 'throw': 622,\n",
       " 'chews': 623,\n",
       " 'bowl': 624,\n",
       " 'worked': 625,\n",
       " 'shop': 626,\n",
       " 'pot': 627,\n",
       " 'fun': 628,\n",
       " 'pure': 629,\n",
       " 'thick': 630,\n",
       " 'satisfying': 631,\n",
       " 'done': 632,\n",
       " 'crazy': 633,\n",
       " 'stay': 634,\n",
       " 'vet': 635,\n",
       " 'yogurt': 636,\n",
       " 'leaves': 637,\n",
       " 'combination': 638,\n",
       " 'future': 639,\n",
       " 'finding': 640,\n",
       " 'true': 641,\n",
       " 'ingredient': 642,\n",
       " 'chili': 643,\n",
       " 'yum': 644,\n",
       " 'perfectly': 645,\n",
       " 'pet': 646,\n",
       " 'bland': 647,\n",
       " 'pricey': 648,\n",
       " 'substitute': 649,\n",
       " 'opinion': 650,\n",
       " 'area': 651,\n",
       " 'okay': 652,\n",
       " 'wow': 653,\n",
       " 'sell': 654,\n",
       " 'recipes': 655,\n",
       " 'almond': 656,\n",
       " 'raw': 657,\n",
       " 'become': 658,\n",
       " 'batch': 659,\n",
       " 'yes': 660,\n",
       " 'expecting': 661,\n",
       " 'completely': 662,\n",
       " 'summer': 663,\n",
       " 'entire': 664,\n",
       " 'packet': 665,\n",
       " 'five': 666,\n",
       " 'spices': 667,\n",
       " 'formula': 668,\n",
       " 'mint': 669,\n",
       " 'today': 670,\n",
       " 'maple': 671,\n",
       " 'decent': 672,\n",
       " 'option': 673,\n",
       " 'baked': 674,\n",
       " 'priced': 675,\n",
       " 'simple': 676,\n",
       " 'horrible': 677,\n",
       " 'pick': 678,\n",
       " 'finish': 679,\n",
       " 'seemed': 680,\n",
       " 'content': 681,\n",
       " 'none': 682,\n",
       " 'addition': 683,\n",
       " 'dish': 684,\n",
       " 'newman': 685,\n",
       " 'costco': 686,\n",
       " 'satisfied': 687,\n",
       " 'later': 688,\n",
       " 'twice': 689,\n",
       " 'kick': 690,\n",
       " 'mind': 691,\n",
       " 'discovered': 692,\n",
       " 'customer': 693,\n",
       " 'truly': 694,\n",
       " 'hoping': 695,\n",
       " 'favorites': 696,\n",
       " 'world': 697,\n",
       " 'carb': 698,\n",
       " 'normally': 699,\n",
       " 'count': 700,\n",
       " 'grain': 701,\n",
       " 'thin': 702,\n",
       " 'pancakes': 703,\n",
       " 'mixes': 704,\n",
       " 'tiny': 705,\n",
       " 'mess': 706,\n",
       " 'hint': 707,\n",
       " 'terrible': 708,\n",
       " 'bottom': 709,\n",
       " 'strawberry': 710,\n",
       " 'particular': 711,\n",
       " 'lower': 712,\n",
       " 'dont': 713,\n",
       " 'cooked': 714,\n",
       " 'noticed': 715,\n",
       " 'serve': 716,\n",
       " 'plan': 717,\n",
       " 'told': 718,\n",
       " 'mom': 719,\n",
       " 'hour': 720,\n",
       " 'unless': 721,\n",
       " 'door': 722,\n",
       " 'contains': 723,\n",
       " 'piece': 724,\n",
       " 'varieties': 725,\n",
       " 'awful': 726,\n",
       " 'prices': 727,\n",
       " 'body': 728,\n",
       " 'remember': 729,\n",
       " 'forward': 730,\n",
       " 'grey': 731,\n",
       " 'six': 732,\n",
       " 'vinegar': 733,\n",
       " 'blue': 734,\n",
       " 'issues': 735,\n",
       " 'bones': 736,\n",
       " 'label': 737,\n",
       " 'seen': 738,\n",
       " 'pie': 739,\n",
       " 'warm': 740,\n",
       " 'glass': 741,\n",
       " 'normal': 742,\n",
       " 'description': 743,\n",
       " 'roasted': 744,\n",
       " 'loose': 745,\n",
       " 'adds': 746,\n",
       " 'oh': 747,\n",
       " 'switch': 748,\n",
       " 'anyway': 749,\n",
       " 'everyday': 750,\n",
       " 'supermarket': 751,\n",
       " 'turned': 752,\n",
       " 'italian': 753,\n",
       " 'paid': 754,\n",
       " 'meals': 755,\n",
       " 'subscription': 756,\n",
       " 'return': 757,\n",
       " 'sweetener': 758,\n",
       " 'otherwise': 759,\n",
       " 'acid': 760,\n",
       " 'veggies': 761,\n",
       " 'stronger': 762,\n",
       " 'sticks': 763,\n",
       " 'bean': 764,\n",
       " 'drinker': 765,\n",
       " 'agree': 766,\n",
       " 'pleasant': 767,\n",
       " 'bring': 768,\n",
       " 'unlike': 769,\n",
       " 'felt': 770,\n",
       " 'ready': 771,\n",
       " 'mountain': 772,\n",
       " 'bears': 773,\n",
       " 'impressed': 774,\n",
       " 'liquid': 775,\n",
       " 'pods': 776,\n",
       " 'individual': 777,\n",
       " 'job': 778,\n",
       " 'taking': 779,\n",
       " 'touch': 780,\n",
       " 'salmon': 781,\n",
       " 'shape': 782,\n",
       " 'carrying': 783,\n",
       " 'earl': 784,\n",
       " 'candies': 785,\n",
       " 'ship': 786,\n",
       " 'uses': 787,\n",
       " 'contain': 788,\n",
       " 'feeling': 789,\n",
       " 'fit': 790,\n",
       " 'thinking': 791,\n",
       " 'matter': 792,\n",
       " 'homemade': 793,\n",
       " 'called': 794,\n",
       " 'office': 795,\n",
       " 'skin': 796,\n",
       " 'point': 797,\n",
       " 'changed': 798,\n",
       " 'crisp': 799,\n",
       " 'thrilled': 800,\n",
       " 'allergies': 801,\n",
       " 'party': 802,\n",
       " 'hands': 803,\n",
       " 'craving': 804,\n",
       " 'garlic': 805,\n",
       " 'caramel': 806,\n",
       " 'feeding': 807,\n",
       " 'results': 808,\n",
       " 'overpowering': 809,\n",
       " 'sealed': 810,\n",
       " 'somewhat': 811,\n",
       " 'chewing': 812,\n",
       " 'convenience': 813,\n",
       " 'immediately': 814,\n",
       " 'plenty': 815,\n",
       " 'set': 816,\n",
       " 'prepare': 817,\n",
       " 'ask': 818,\n",
       " 'overly': 819,\n",
       " 'nicely': 820,\n",
       " 'send': 821,\n",
       " 'flavoring': 822,\n",
       " 'chocolates': 823,\n",
       " 'berry': 824,\n",
       " 'shake': 825,\n",
       " 'carbs': 826,\n",
       " 'complaint': 827,\n",
       " 'wrapped': 828,\n",
       " 'breath': 829,\n",
       " 'raspberry': 830,\n",
       " 'eggs': 831,\n",
       " 'picture': 832,\n",
       " 'heavy': 833,\n",
       " 'pizza': 834,\n",
       " 'nearly': 835,\n",
       " 'dishes': 836,\n",
       " 'bigger': 837,\n",
       " 'lime': 838,\n",
       " 'afternoon': 839,\n",
       " 'gummy': 840,\n",
       " 'expiration': 841,\n",
       " 'style': 842,\n",
       " 'worst': 843,\n",
       " 'sea': 844,\n",
       " 'benefits': 845,\n",
       " 'mango': 846,\n",
       " 'fairly': 847,\n",
       " 'usual': 848,\n",
       " 'beverage': 849,\n",
       " 'crispy': 850,\n",
       " 'kitchen': 851,\n",
       " 'peanuts': 852,\n",
       " 'types': 853,\n",
       " 'share': 854,\n",
       " 'plant': 855,\n",
       " 'sick': 856,\n",
       " 'supply': 857,\n",
       " 'except': 858,\n",
       " 'person': 859,\n",
       " 'check': 860,\n",
       " 'source': 861,\n",
       " 'stuck': 862,\n",
       " 'mostly': 863,\n",
       " 'addicted': 864,\n",
       " 'served': 865,\n",
       " 'stevia': 866,\n",
       " 'call': 867,\n",
       " 'dented': 868,\n",
       " 'brought': 869,\n",
       " 'children': 870,\n",
       " 'near': 871,\n",
       " 'bbq': 872,\n",
       " 'lover': 873,\n",
       " 'nutrition': 874,\n",
       " 'gf': 875,\n",
       " 'supposed': 876,\n",
       " 'reading': 877,\n",
       " 'balance': 878,\n",
       " 'jelly': 879,\n",
       " 'pouch': 880,\n",
       " 'perhaps': 881,\n",
       " 'quantity': 882,\n",
       " 'mother': 883,\n",
       " 'walmart': 884,\n",
       " 'chance': 885,\n",
       " 'biscuits': 886,\n",
       " 'kinds': 887,\n",
       " 'trouble': 888,\n",
       " 'grams': 889,\n",
       " 'short': 890,\n",
       " 'weird': 891,\n",
       " 'ended': 892,\n",
       " 'running': 893,\n",
       " 'means': 894,\n",
       " 'jars': 895,\n",
       " 'replacement': 896,\n",
       " 'kept': 897,\n",
       " 'kid': 898,\n",
       " 'outside': 899,\n",
       " 'coming': 900,\n",
       " 'lbs': 901,\n",
       " 'alot': 902,\n",
       " 'pleasantly': 903,\n",
       " 'issue': 904,\n",
       " 'sorry': 905,\n",
       " 'fill': 906,\n",
       " 'standard': 907,\n",
       " 'brewed': 908,\n",
       " 'hate': 909,\n",
       " 'higher': 910,\n",
       " 'enjoys': 911,\n",
       " 'brewing': 912,\n",
       " 'dressing': 913,\n",
       " 'bake': 914,\n",
       " 'reviewers': 915,\n",
       " 'personally': 916,\n",
       " 'gummi': 917,\n",
       " 'heard': 918,\n",
       " 'gotten': 919,\n",
       " 'disappointing': 920,\n",
       " 'alone': 921,\n",
       " 'pops': 922,\n",
       " 'directions': 923,\n",
       " 'unique': 924,\n",
       " 'vegetables': 925,\n",
       " 'reviewer': 926,\n",
       " 'knew': 927,\n",
       " 'blends': 928,\n",
       " 'birthday': 929,\n",
       " 'blood': 930,\n",
       " 'pounds': 931,\n",
       " 'whatever': 932,\n",
       " 'grains': 933,\n",
       " 'gourmet': 934,\n",
       " 'portion': 935,\n",
       " 'working': 936,\n",
       " 'paying': 937,\n",
       " 'surprise': 938,\n",
       " 'number': 939,\n",
       " 'sort': 940,\n",
       " 'pour': 941,\n",
       " 'wine': 942,\n",
       " 'dessert': 943,\n",
       " 'sensitive': 944,\n",
       " 'cashews': 945,\n",
       " 'double': 946,\n",
       " 'china': 947,\n",
       " 'beautiful': 948,\n",
       " 'bitterness': 949,\n",
       " 'sample': 950,\n",
       " 'shelf': 951,\n",
       " 'tuna': 952,\n",
       " 'clear': 953,\n",
       " 'busy': 954,\n",
       " 'notice': 955,\n",
       " 'threw': 956,\n",
       " 'whenever': 957,\n",
       " 'straight': 958,\n",
       " 'cause': 959,\n",
       " 'mustard': 960,\n",
       " 'eater': 961,\n",
       " 'sauces': 962,\n",
       " 'fair': 963,\n",
       " 'receive': 964,\n",
       " 'helped': 965,\n",
       " 'brownies': 966,\n",
       " 'covered': 967,\n",
       " 'school': 968,\n",
       " 'careful': 969,\n",
       " 'tart': 970,\n",
       " 'potatoes': 971,\n",
       " 'fix': 972,\n",
       " 'sitting': 973,\n",
       " 'wants': 974,\n",
       " 'beer': 975,\n",
       " 'selling': 976,\n",
       " 'bits': 977,\n",
       " 'cracker': 978,\n",
       " 'note': 979,\n",
       " 'vegan': 980,\n",
       " 'creamer': 981,\n",
       " 'listed': 982,\n",
       " 'soups': 983,\n",
       " 'turn': 984,\n",
       " 'cool': 985,\n",
       " 'offered': 986,\n",
       " 'restaurant': 987,\n",
       " 'fridge': 988,\n",
       " 'mini': 989,\n",
       " 'cheddar': 990,\n",
       " 'tree': 991,\n",
       " 'pantry': 992,\n",
       " 'dairy': 993,\n",
       " 'switched': 994,\n",
       " 'tired': 995,\n",
       " 'lost': 996,\n",
       " 'instructions': 997,\n",
       " 'website': 998,\n",
       " 'boost': 999,\n",
       " 'satisfy': 1000,\n",
       " ...}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "src_tokenizer.word_index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "forbidden-queensland",
   "metadata": {},
   "source": [
    "> 여기서 등장 빈도가 7회 미만인 건 연산량을 줄이기 위해 제외할 예정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "sustainable-duplicate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "odict_items([('use', 5126), ('product', 10814), ('dozens', 22), ('baking', 417), ('needs', 458), ('son', 925), ('allergic', 183), ('eggs', 261), ('dairy', 210), ('gluten', 1555), ('intolerant', 68), ('savior', 5), ('house', 918), ('good', 14861), ('loaf', 63), ('homemade', 278), ('bread', 999), ('fresh', 2128), ('bagels', 25), ('every', 2352), ('holiday', 112), ('season', 126), ('husband', 1311), ('makes', 2377), ('vegan', 213), ('fruit', 1200), ('cakes', 145), ('send', 265), ('relatives', 31), ('thanks', 851), ('pamela', 125), ('nervous', 37), ('ordering', 1031), ('one', 10008), ('reviewer', 229), ('said', 1024), ('ordered', 2489), ('two', 2417), ('bags', 1620), ('problem', 1008), ('rat', 20), ('terrier', 99), ('daughter', 841), ('standard', 236), ('schnauzer', 28), ('love', 10399), ('food', 5469), ('rejected', 8), ('several', 1174), ('brands', 1207), ('flavors', 2431), ('found', 3383), ('coats', 86), ('beautiful', 223), ('digest', 82), ('well', 4426), ('compare', 203), 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('hoot', 1), ('weighty', 1), ('crampy', 1), ('gaseous', 1), ('sprang', 1), ('annihilated', 1), ('nondistict', 1), ('delectables', 1), ('oghi', 1), ('kilter', 1), ('allert', 1), ('terrrible', 1), ('forseeable', 1), ('mrning', 1), ('befire', 1), ('benson', 1), ('asparaus', 1), ('tenderizer', 1), ('ahbl', 1), ('potates', 1), ('curves', 1), ('stregnth', 1), ('desolved', 1), ('marigold', 1), ('bouche', 1), ('ewwwwww', 1), ('verities', 1), ('gargle', 1), ('caning', 1), ('gsds', 1), ('gassing', 1), ('maniacs', 1), ('mvd', 1), ('arose', 1), ('roastgood', 1), ('peeked', 1), ('altzheimers', 1), ('tobut', 1), ('versionsto', 1), ('yuuuumy', 1), ('heals', 1), ('jolts', 1), ('mmn', 1), ('fines', 1), ('decomposable', 1), ('bageland', 1), ('beanitos', 1), ('intervention', 1), ('andhas', 1), ('wilhi', 1), ('markups', 1), ('devoure', 1), ('backbone', 1), ('yeal', 1), ('ambivalence', 1), ('interpret', 1), ('supporter', 1), ('aweosme', 1), ('sprinted', 1), ('bagvery', 1), ('sodatream', 1), ('mai', 1), ('tais', 1), ('rejuvenation', 1), ('sweenter', 1), ('wincing', 1), ('elgible', 1), ('theywill', 1), ('andsometimes', 1), ('onhow', 1), ('sheeats', 1), ('themagain', 1), ('anton', 1), ('strudels', 1), ('ou', 1), ('headless', 1), ('vainy', 1), ('perfectpackaged', 1), ('schiffer', 1), ('fanstastic', 1), ('quaint', 1), ('worh', 1), ('walloping', 1), ('massages', 1), ('sopus', 1), ('brilliantly', 1), ('poopin', 1), ('mesclun', 1), ('massaman', 1), ('gia', 1), ('russa', 1), ('circulation', 1), ('appreication', 1), ('gerbera', 2), ('daisies', 2), ('inexpenisve', 1), ('depressant', 1), ('musselmans', 2), ('whitehouse', 2), ('pembroke', 1), ('satifyingly', 1), ('overriding', 1), ('olivey', 1), ('bruchetta', 1), ('gnarly', 1), ('fisherman', 1), ('fleet', 1), ('tarty', 1), ('dishearting', 1), ('rold', 1), ('orchards', 1), ('reccommendation', 1), ('delivert', 1), ('leigh', 1), ('choclatique', 1), ('pith', 1)])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 빈도 수 확인\n",
    "src_tokenizer.word_counts.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "forward-visit",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('use', 5126),\n",
       "             ('product', 10814),\n",
       "             ('dozens', 22),\n",
       "             ('baking', 417),\n",
       "             ('needs', 458),\n",
       "             ('son', 925),\n",
       "             ('allergic', 183),\n",
       "             ('eggs', 261),\n",
       "             ('dairy', 210),\n",
       "             ('gluten', 1555),\n",
       "             ('intolerant', 68),\n",
       "             ('savior', 5),\n",
       "             ('house', 918),\n",
       "             ('good', 14861),\n",
       "             ('loaf', 63),\n",
       "             ('homemade', 278),\n",
       "             ('bread', 999),\n",
       "             ('fresh', 2128),\n",
       "             ('bagels', 25),\n",
       "             ('every', 2352),\n",
       "             ('holiday', 112),\n",
       "             ('season', 126),\n",
       "             ('husband', 1311),\n",
       "             ('makes', 2377),\n",
       "             ('vegan', 213),\n",
       "             ('fruit', 1200),\n",
       "             ('cakes', 145),\n",
       "             ('send', 265),\n",
       "             ('relatives', 31),\n",
       "             ('thanks', 851),\n",
       "             ('pamela', 125),\n",
       "             ('nervous', 37),\n",
       "             ('ordering', 1031),\n",
       "             ('one', 10008),\n",
       "             ('reviewer', 229),\n",
       "             ('said', 1024),\n",
       "             ('ordered', 2489),\n",
       "             ('two', 2417),\n",
       "             ('bags', 1620),\n",
       "             ('problem', 1008),\n",
       "             ('rat', 20),\n",
       "             ('terrier', 99),\n",
       "             ('daughter', 841),\n",
       "             ('standard', 236),\n",
       "             ('schnauzer', 28),\n",
       "             ('love', 10399),\n",
       "             ('food', 5469),\n",
       "             ('rejected', 8),\n",
       "             ('several', 1174),\n",
       "             ('brands', 1207),\n",
       "             ('flavors', 2431),\n",
       "             ('found', 3383),\n",
       "             ('coats', 86),\n",
       "             ('beautiful', 223),\n",
       "             ('digest', 82),\n",
       "             ('well', 4426),\n",
       "             ('compare', 203),\n",
       "             ('made', 2896),\n",
       "             ('batches', 57),\n",
       "             ('chocolate', 4300),\n",
       "             ('ice', 701),\n",
       "             ('cream', 847),\n",
       "             ('identical', 35),\n",
       "             ('recipe', 526),\n",
       "             ('ghiradelli', 21),\n",
       "             ('cocoa', 646),\n",
       "             ('pernigotti', 6),\n",
       "             ('could', 3315),\n",
       "             ('immediately', 267),\n",
       "             ('taste', 11624),\n",
       "             ('flavor', 9838),\n",
       "             ('difference', 553),\n",
       "             ('much', 5641),\n",
       "             ('chocolaty', 48),\n",
       "             ('worth', 1563),\n",
       "             ('money', 1250),\n",
       "             ('fan', 842),\n",
       "             ('ultimate', 55),\n",
       "             ('drink', 3266),\n",
       "             ('smells', 559),\n",
       "             ('tastes', 3238),\n",
       "             ('rich', 944),\n",
       "             ('almost', 1333),\n",
       "             ('like', 15703),\n",
       "             ('golden', 153),\n",
       "             ('fluid', 21),\n",
       "             ('swear', 42),\n",
       "             ('feels', 150),\n",
       "             ('velvet', 26),\n",
       "             ('going', 1419),\n",
       "             ('throat', 153),\n",
       "             ('extent', 13),\n",
       "             ('willing', 72),\n",
       "             ('transported', 7),\n",
       "             ('norway', 4),\n",
       "             ('blame', 25),\n",
       "             ('friend', 581),\n",
       "             ('introduced', 174),\n",
       "             ('lovely', 153),\n",
       "             ('spiced', 64),\n",
       "             ('tea', 8053),\n",
       "             ('first', 3068),\n",
       "             ('pot', 369),\n",
       "             ('wonderful', 1975),\n",
       "             ('coffee', 9985),\n",
       "             ('simply', 489),\n",
       "             ('delish', 52),\n",
       "             ('subtle', 206),\n",
       "             ('hint', 324),\n",
       "             ('cinnamon', 536),\n",
       "             ('overpowering', 269),\n",
       "             ('worried', 131),\n",
       "             ('cups', 1621),\n",
       "             ('already', 441),\n",
       "             ('making', 981),\n",
       "             ('lots', 509),\n",
       "             ('difficult', 397),\n",
       "             ('find', 5041),\n",
       "             ('flavored', 1055),\n",
       "             ('types', 256),\n",
       "             ('decaf', 614),\n",
       "             ('thumbs', 81),\n",
       "             ('refuses', 35),\n",
       "             ('eat', 4112),\n",
       "             ('fish', 414),\n",
       "             ('loves', 3201),\n",
       "             ('takes', 438),\n",
       "             ('minutes', 653),\n",
       "             ('make', 4355),\n",
       "             ('way', 2879),\n",
       "             ('better', 4615),\n",
       "             ('kind', 1078),\n",
       "             ('tuna', 222),\n",
       "             ('helper', 66),\n",
       "             ('tried', 4850),\n",
       "             ('besides', 106),\n",
       "             ('box', 2775),\n",
       "             ('need', 1838),\n",
       "             ('tablespoon', 97),\n",
       "             ('butter', 1261),\n",
       "             ('cup', 3270),\n",
       "             ('milkit', 1),\n",
       "             ('breeze', 31),\n",
       "             ('prepare', 266),\n",
       "             ('leave', 391),\n",
       "             ('alone', 232),\n",
       "             ('put', 1696),\n",
       "             ('pasta', 950),\n",
       "             ('milk', 1892),\n",
       "             ('likes', 824),\n",
       "             ('boil', 97),\n",
       "             ('keep', 2060),\n",
       "             ('eye', 71),\n",
       "             ('reduce', 71),\n",
       "             ('heat', 431),\n",
       "             ('right', 2224),\n",
       "             ('starts', 111),\n",
       "             ('boiling', 90),\n",
       "             ('morning', 1189),\n",
       "             ('joe', 153),\n",
       "             ('able', 1074),\n",
       "             ('terrible', 324),\n",
       "             ('baked', 345),\n",
       "             ('yet', 778),\n",
       "             ('dont', 322),\n",
       "             ('know', 2211),\n",
       "             ('goods', 89),\n",
       "             ('amazed', 105),\n",
       "             ('got', 2631),\n",
       "             ('small', 1987),\n",
       "             ('price', 5929),\n",
       "             ('best', 5869),\n",
       "             ('tasted', 1701),\n",
       "             ('keurig', 812),\n",
       "             ('machine', 659),\n",
       "             ('bold', 778),\n",
       "             ('dark', 1324),\n",
       "             ('order', 3329),\n",
       "             ('within', 387),\n",
       "             ('week', 666),\n",
       "             ('highly', 1868),\n",
       "             ('recommend', 3270),\n",
       "             ('another', 1249),\n",
       "             ('great', 14407),\n",
       "             ('easy', 2526),\n",
       "             ('fix', 214),\n",
       "             ('soup', 730),\n",
       "             ('add', 2128),\n",
       "             ('ham', 66),\n",
       "             ('onions', 85),\n",
       "             ('barley', 63),\n",
       "             ('cut', 529),\n",
       "             ('sodium', 381),\n",
       "             ('abit', 6),\n",
       "             ('extra', 850),\n",
       "             ('water', 2538),\n",
       "             ('seems', 982),\n",
       "             ('trick', 149),\n",
       "             ('enjoy', 1985),\n",
       "             ('coconut', 1188),\n",
       "             ('received', 1476),\n",
       "             ('exactly', 594),\n",
       "             ('expected', 681),\n",
       "             ('packaging', 958),\n",
       "             ('little', 5098),\n",
       "             ('bit', 2477),\n",
       "             ('bottles', 501),\n",
       "             ('loose', 301),\n",
       "             ('looking', 1680),\n",
       "             ('something', 2060),\n",
       "             ('replace', 129),\n",
       "             ('millstone', 7),\n",
       "             ('foglifter', 7),\n",
       "             ('outrageous', 46),\n",
       "             ('wile', 2),\n",
       "             ('quit', 94),\n",
       "             ('fit', 279),\n",
       "             ('bill', 187),\n",
       "             ('reorder', 129),\n",
       "             ('potential', 24),\n",
       "             ('customers', 103),\n",
       "             ('item', 1376),\n",
       "             ('sugar', 3194),\n",
       "             ('free', 3037),\n",
       "             ('form', 181),\n",
       "             ('general', 166),\n",
       "             ('chai', 473),\n",
       "             ('latte', 133),\n",
       "             ('talking', 63),\n",
       "             ('calories', 1327),\n",
       "             ('per', 1033),\n",
       "             ('serving', 536),\n",
       "             ('rather', 663),\n",
       "             ('also', 4454),\n",
       "             ('high', 1560),\n",
       "             ('calorie', 492),\n",
       "             ('local', 1924),\n",
       "             ('edible', 143),\n",
       "             ('writing', 57),\n",
       "             ('markers', 41),\n",
       "             ('none', 341),\n",
       "             ('number', 226),\n",
       "             ('colors', 132),\n",
       "             ('worked', 370),\n",
       "             ('online', 761),\n",
       "             ('get', 6432),\n",
       "             ('cake', 573),\n",
       "             ('pops', 232),\n",
       "             ('excellent', 1932),\n",
       "             ('pizza', 260),\n",
       "             ('dough', 92),\n",
       "             ('sure', 1784),\n",
       "             ('would', 9218),\n",
       "             ('texture', 1537),\n",
       "             ('crumbly', 93),\n",
       "             ('holds', 118),\n",
       "             ('together', 374),\n",
       "             ('even', 3875),\n",
       "             ('freezing', 27),\n",
       "             ('really', 6471),\n",
       "             ('sorry', 237),\n",
       "             ('read', 640),\n",
       "             ('reviews', 958),\n",
       "             ('timothy', 90),\n",
       "             ('variety', 974),\n",
       "             ('disappointment', 145),\n",
       "             ('gloria', 58),\n",
       "             ('jean', 47),\n",
       "             ('definitely', 2117),\n",
       "             ('took', 701),\n",
       "             ('candy', 1406),\n",
       "             ('described', 180),\n",
       "             ('middle', 170),\n",
       "             ('hard', 2098),\n",
       "             ('silky', 37),\n",
       "             ('smooth', 1045),\n",
       "             ('filling', 491),\n",
       "             ('looks', 500),\n",
       "             ('past', 494),\n",
       "             ('expiration', 259),\n",
       "             ('date', 485),\n",
       "             ('never', 2383),\n",
       "             ('reccommend', 30),\n",
       "             ('paid', 298),\n",
       "             ('chunk', 44),\n",
       "             ('cash', 12),\n",
       "             ('nothing', 1041),\n",
       "             ('used', 3230),\n",
       "             ('buy', 5847),\n",
       "             ('kadoya', 5),\n",
       "             ('pure', 367),\n",
       "             ('sesame', 124),\n",
       "             ('oil', 1374),\n",
       "             ('directly', 162),\n",
       "             ('manufacturer', 130),\n",
       "             ('case', 1038),\n",
       "             ('time', 5180),\n",
       "             ('happy', 1995),\n",
       "             ('amazon', 6118),\n",
       "             ('stocks', 13),\n",
       "             ('give', 2477),\n",
       "             ('try', 4184),\n",
       "             ('delicious', 3484),\n",
       "             ('hooked', 406),\n",
       "             ('everything', 814),\n",
       "             ('frying', 51),\n",
       "             ('yum', 362),\n",
       "             ('switch', 300),\n",
       "             ('deet', 9),\n",
       "             ('based', 372),\n",
       "             ('repellent', 2),\n",
       "             ('nice', 2950),\n",
       "             ('light', 1036),\n",
       "             ('scent', 155),\n",
       "             ('oily', 134),\n",
       "             ('works', 1038),\n",
       "             ('without', 2257),\n",
       "             ('walking', 29),\n",
       "             ('dinner', 501),\n",
       "             ('table', 154),\n",
       "             ('mosquitos', 3),\n",
       "             ('brand', 2241),\n",
       "             ('non', 411),\n",
       "             ('spf', 2),\n",
       "             ('felt', 290),\n",
       "             ('sticky', 190),\n",
       "             ('sticking', 79),\n",
       "             ('cutter', 9),\n",
       "             ('sunscreen', 3),\n",
       "             ('separately', 33),\n",
       "             ('whole', 1664),\n",
       "             ('family', 1531),\n",
       "             ('freeze', 204),\n",
       "             ('dried', 593),\n",
       "             ('packs', 539),\n",
       "             ('convenient', 590),\n",
       "             ('travel', 175),\n",
       "             ('benefits', 258),\n",
       "             ('pomegranate', 193),\n",
       "             ('wanted', 826),\n",
       "             ('tart', 215),\n",
       "             ('refreshing', 549),\n",
       "             ('decided', 713),\n",
       "             ('boy', 145),\n",
       "             ('beverage', 257),\n",
       "             ('subscribe', 547),\n",
       "             ('save', 997),\n",
       "             ('shipping', 1843),\n",
       "             ('twice', 338),\n",
       "             ('month', 855),\n",
       "             ('regular', 1777),\n",
       "             ('consumer', 43),\n",
       "             ('affiliated', 1),\n",
       "             ('anyway', 300),\n",
       "             ('izze', 37),\n",
       "             ('enjoyed', 788),\n",
       "             ('ahmad', 22),\n",
       "             ('cardamon', 7),\n",
       "             ('year', 1405),\n",
       "             ('drinker', 292),\n",
       "             ('teas', 804),\n",
       "             ('superior', 185),\n",
       "             ('ways', 131),\n",
       "             ('particular', 323),\n",
       "             ('deep', 160),\n",
       "             ('robust', 195),\n",
       "             ('bitter', 1096),\n",
       "             ('wake', 117),\n",
       "             ('shared', 159),\n",
       "             ('friends', 806),\n",
       "             ('loved', 1590),\n",
       "             ('mango', 258),\n",
       "             ('apricot', 46),\n",
       "             ('earl', 283),\n",
       "             ('grey', 310),\n",
       "             ('evening', 196),\n",
       "             ('beans', 760),\n",
       "             ('prescribed', 13),\n",
       "             ('amount', 1174),\n",
       "             ('opened', 616),\n",
       "             ('package', 1537),\n",
       "             ('knew', 229),\n",
       "             ('amazing', 875),\n",
       "             ('aroma', 596),\n",
       "             ('evident', 14),\n",
       "             ('intensified', 3),\n",
       "             ('actually', 1372),\n",
       "             ('bought', 3712),\n",
       "             ('explicitly', 3),\n",
       "             ('vanilla', 830),\n",
       "             ('extract', 171),\n",
       "             ('say', 1890),\n",
       "             ('juicy', 71),\n",
       "             ('still', 2407),\n",
       "             ('kid', 239),\n",
       "             ('heart', 139),\n",
       "             ('occasionally', 86),\n",
       "             ('purchase', 1861),\n",
       "             ('rock', 112),\n",
       "             ('shops', 75),\n",
       "             ('quality', 2272),\n",
       "             ('half', 1144),\n",
       "             ('delivered', 518),\n",
       "             ('door', 316),\n",
       "             ('stars', 812),\n",
       "             ('cost', 802),\n",
       "             ('stick', 470),\n",
       "             ('christmas', 534),\n",
       "             ('gifts', 194),\n",
       "             ('green', 1473),\n",
       "             ('passion', 46),\n",
       "             ('far', 1777),\n",
       "             ('favorite', 3193),\n",
       "             ('star', 431),\n",
       "             ('lemon', 633),\n",
       "             ('awesome', 827),\n",
       "             ('rest', 390),\n",
       "             ('yogurt', 365),\n",
       "             ('snacks', 667),\n",
       "             ('staple', 158),\n",
       "             ('old', 1921),\n",
       "             ('diet', 1113),\n",
       "             ('eats', 377),\n",
       "             ('day', 2511),\n",
       "             ('town', 158),\n",
       "             ('complaints', 124),\n",
       "             ('last', 1453),\n",
       "             ('night', 490),\n",
       "             ('thrilled', 275),\n",
       "             ('smelled', 163),\n",
       "             ('unbelievable', 42),\n",
       "             ('toppings', 42),\n",
       "             ('let', 638),\n",
       "             ('piece', 315),\n",
       "             ('telling', 43),\n",
       "             ('guessed', 13),\n",
       "             ('thought', 1752),\n",
       "             ('crust', 162),\n",
       "             ('came', 1372),\n",
       "             ('crunchy', 803),\n",
       "             ('outside', 239),\n",
       "             ('fluffy', 104),\n",
       "             ('inside', 442),\n",
       "             ('anyone', 875),\n",
       "             ('unable', 127),\n",
       "             ('promise', 33),\n",
       "             ('miss', 192),\n",
       "             ('beat', 418),\n",
       "             ('hcg', 19),\n",
       "             ('filler', 54),\n",
       "             ('noodles', 680),\n",
       "             ('come', 1040),\n",
       "             ('close', 485),\n",
       "             ('begin', 56),\n",
       "             ('smell', 1191),\n",
       "             ('nasty', 207),\n",
       "             ('open', 744),\n",
       "             ('bag', 3195),\n",
       "             ('clean', 573),\n",
       "             ('blanch', 6),\n",
       "             ('absolutely', 1189),\n",
       "             ('gross', 160),\n",
       "             ('faint', 35),\n",
       "             ('smartbones', 7),\n",
       "             ('treat', 1946),\n",
       "             ('dogss', 1),\n",
       "             ('dachushunds', 1),\n",
       "             ('peanut', 961),\n",
       "             ('may', 1040),\n",
       "             ('corn', 688),\n",
       "             ('chips', 1920),\n",
       "             ('salt', 1719),\n",
       "             ('trouble', 242),\n",
       "             ('sky', 19),\n",
       "             ('bar', 1209),\n",
       "             ('specific', 56),\n",
       "             ('parts', 77),\n",
       "             ('knowing', 146),\n",
       "             ('segment', 6),\n",
       "             ('bars', 1637),\n",
       "             ('rule', 20),\n",
       "             ('pecan', 86),\n",
       "             ('strong', 2055),\n",
       "             ('fennel', 15),\n",
       "             ('addition', 341),\n",
       "             ('blends', 229),\n",
       "             ('packaged', 565),\n",
       "             ('shown', 74),\n",
       "             ('picture', 261),\n",
       "             ('timely', 202),\n",
       "             ('manner', 129),\n",
       "             ('ww', 28),\n",
       "             ('oz', 1094),\n",
       "             ('points', 119),\n",
       "             ('enjoying', 191),\n",
       "             ('carries', 157),\n",
       "             ('noon', 15),\n",
       "             ('lunch', 491),\n",
       "             ('san', 172),\n",
       "             ('francisco', 121),\n",
       "             ('bay', 142),\n",
       "             ('rainforest', 35),\n",
       "             ('blend', 1224),\n",
       "             ('fog', 100),\n",
       "             ('chaser', 92),\n",
       "             ('wow', 356),\n",
       "             ('stronger', 294),\n",
       "             ('offers', 118),\n",
       "             ('change', 413),\n",
       "             ('bargain', 143),\n",
       "             ('crispy', 257),\n",
       "             ('tangy', 168),\n",
       "             ('healthy', 2133),\n",
       "             ('feel', 1302),\n",
       "             ('guilt', 135),\n",
       "             ('eating', 1726),\n",
       "             ('ask', 266),\n",
       "             ('wish', 1130),\n",
       "             ('banana', 376),\n",
       "             ('version', 601),\n",
       "             ('stuff', 2290),\n",
       "             ('mother', 244),\n",
       "             ('grocery', 1472),\n",
       "             ('stores', 1701),\n",
       "             ('live', 539),\n",
       "             ('uses', 281),\n",
       "             ('called', 278),\n",
       "             ('chessbars', 1),\n",
       "             ('reordering', 49),\n",
       "             ('run', 493),\n",
       "             ('low', 1551),\n",
       "             ('sell', 356),\n",
       "             ('lol', 186),\n",
       "             ('dog', 4073),\n",
       "             ('chew', 694),\n",
       "             ('become', 353),\n",
       "             ('parson', 1),\n",
       "             ('russell', 31),\n",
       "             ('heavy', 261),\n",
       "             ('chewer', 80),\n",
       "             ('denta', 2),\n",
       "             ('dinos', 8),\n",
       "             ('cherry', 498),\n",
       "             ('blossom', 16),\n",
       "             ('teeth', 839),\n",
       "             ('pleasant', 291),\n",
       "             ('pack', 1660),\n",
       "             ('enough', 1939),\n",
       "             ('pieces', 834),\n",
       "             ('bring', 291),\n",
       "             ('along', 408),\n",
       "             ('traveling', 89),\n",
       "             ('gave', 916),\n",
       "             ('yr', 104),\n",
       "             ('sweet', 3337),\n",
       "             ('people', 1223),\n",
       "             ('ages', 44),\n",
       "             ('easily', 570),\n",
       "             ('approve', 7),\n",
       "             ('certainly', 398),\n",
       "             ('hesitate', 70),\n",
       "             ('everyone', 859),\n",
       "             ('bigelow', 104),\n",
       "             ('english', 202),\n",
       "             ('breakfast', 1017),\n",
       "             ('kcups', 92),\n",
       "             ('new', 1270),\n",
       "             ('relaxing', 59),\n",
       "             ('end', 490),\n",
       "             ('organic', 1809),\n",
       "             ('different', 1889),\n",
       "             ('bad', 1939),\n",
       "             ('agree', 292),\n",
       "             ('buying', 2016),\n",
       "             ('peppercorns', 55),\n",
       "             ('store', 3041),\n",
       "             ('require', 73),\n",
       "             ('loan', 1),\n",
       "             ('gift', 1005),\n",
       "             ('peppercorn', 14),\n",
       "             ('afficianado', 4),\n",
       "             ('brine', 12),\n",
       "             ('alter', 12),\n",
       "             ('dash', 61),\n",
       "             ('cooking', 599),\n",
       "             ('steak', 138),\n",
       "             ('au', 24),\n",
       "             ('poivre', 1),\n",
       "             ('tasty', 2127),\n",
       "             ('provide', 144),\n",
       "             ('consider', 200),\n",
       "             ('grinding', 47),\n",
       "             ('adding', 380),\n",
       "             ('peppery', 49),\n",
       "             ('accent', 5),\n",
       "             ('sauces', 219),\n",
       "             ('gravies', 5),\n",
       "             ('perfect', 2587),\n",
       "             ('wanting', 119),\n",
       "             ('usually', 1062),\n",
       "             ('since', 2534),\n",
       "             ('real', 1373),\n",
       "             ('poodle', 62),\n",
       "             ('treats', 2273),\n",
       "             ('accessible', 2),\n",
       "             ('feeding', 271),\n",
       "             ('storing', 33),\n",
       "             ('recomend', 59),\n",
       "             ('defintely', 3),\n",
       "             ('ingredients', 1498),\n",
       "             ('nutritional', 206),\n",
       "             ('analysis', 8),\n",
       "             ('awful', 314),\n",
       "             ('super', 780),\n",
       "             ('return', 297),\n",
       "             ('left', 538),\n",
       "             ('rip', 81),\n",
       "             ('pay', 501),\n",
       "             ('french', 599),\n",
       "             ('roast', 899),\n",
       "             ('expectations', 142),\n",
       "             ('considering', 200),\n",
       "             ('name', 383),\n",
       "             ('attached', 24),\n",
       "             ('disappointed', 1186),\n",
       "             ('hearty', 95),\n",
       "             ('full', 1239),\n",
       "             ('bodied', 204),\n",
       "             ('leaves', 365),\n",
       "             ('aftertaste', 537),\n",
       "             ('lover', 249),\n",
       "             ('roasts', 83),\n",
       "             ('trying', 1229),\n",
       "             ('kona', 187),\n",
       "             ('island', 54),\n",
       "             ('style', 259),\n",
       "             ('brewers', 72),\n",
       "             ('instead', 931),\n",
       "             ('bitterness', 223),\n",
       "             ('delivers', 85),\n",
       "             ('kick', 338),\n",
       "             ('likely', 135),\n",
       "             ('dogs', 2565),\n",
       "             ('cheap', 375),\n",
       "             ('around', 1157),\n",
       "             ('cans', 849),\n",
       "             ('prices', 314),\n",
       "             ('doubled', 31),\n",
       "             ('served', 252),\n",
       "             ('cat', 1354),\n",
       "             ('bowl', 371),\n",
       "             ('started', 691),\n",
       "             ('ever', 2382),\n",
       "             ('eaten', 472),\n",
       "             ('went', 765),\n",
       "             ('expensive', 1359),\n",
       "             ('ounces', 201),\n",
       "             ('feed', 439),\n",
       "             ('touch', 284),\n",
       "             ('meal', 849),\n",
       "             ('ounce', 649),\n",
       "             ('lower', 323),\n",
       "             ('exceptionally', 35),\n",
       "             ('back', 1679),\n",
       "             ('almond', 354),\n",
       "             ('outstanding', 197),\n",
       "             ('auto', 171),\n",
       "             ('shipment', 403),\n",
       "             ('cannot', 3152),\n",
       "             ('believe', 568),\n",
       "             ('mrs', 60),\n",
       "             ('fields', 9),\n",
       "             ('slightly', 543),\n",
       "             ('front', 130),\n",
       "             ('suspicious', 14),\n",
       "             ('counted', 16),\n",
       "             ('cookies', 1539),\n",
       "             ('realized', 111),\n",
       "             ('someone', 459),\n",
       "             ('pilfered', 2),\n",
       "             ('mistake', 150),\n",
       "             ('trusting', 6),\n",
       "             ('lakewood', 14),\n",
       "             ('candies', 282),\n",
       "             ('sunflower', 109),\n",
       "             ('seeds', 538),\n",
       "             ('larger', 479),\n",
       "             ('flavorful', 675),\n",
       "             ('big', 1514),\n",
       "             ('jumbo', 20),\n",
       "             ('posted', 35),\n",
       "             ('abundance', 15),\n",
       "             ('shell', 69),\n",
       "             ('detract', 8),\n",
       "             ('warning', 82),\n",
       "             ('extremely', 439),\n",
       "             ('addictive', 203),\n",
       "             ('classic', 117),\n",
       "             ('amatriciana', 1),\n",
       "             ('requires', 62),\n",
       "             ('mild', 529),\n",
       "             ('bacon', 131),\n",
       "             ('purchased', 1667),\n",
       "             ('la', 68),\n",
       "             ('quercia', 1),\n",
       "             ('nailed', 8),\n",
       "             ('guanciale', 4),\n",
       "             ('rendered', 6),\n",
       "             ('tender', 173),\n",
       "             ('diced', 37),\n",
       "             ('tomatoes', 145),\n",
       "             ('peccorino', 1),\n",
       "             ('romano', 4),\n",
       "             ('pinch', 77),\n",
       "             ('crushed', 111),\n",
       "             ('red', 596),\n",
       "             ('pepper', 651),\n",
       "             ('done', 366),\n",
       "             ('stands', 52),\n",
       "             ('bucatini', 3),\n",
       "             ('might', 821),\n",
       "             ('rome', 11),\n",
       "             ('packing', 146),\n",
       "             ('cola', 56),\n",
       "             ('flavoring', 265),\n",
       "             ('stevia', 252),\n",
       "             ('coca', 34),\n",
       "             ('sodastream', 14),\n",
       "             ('hey', 71),\n",
       "             ('artificial', 556),\n",
       "             ('sweeteners', 158),\n",
       "             ('always', 2264),\n",
       "             ('quite', 1476),\n",
       "             ('liek', 3),\n",
       "             ('burnt', 126),\n",
       "             ('coffees', 547),\n",
       "             ('community', 47),\n",
       "             ('hazelnut', 204),\n",
       "             ('fairly', 258),\n",
       "             ('pleased', 723),\n",
       "             ('prefer', 899),\n",
       "             ('stash', 202),\n",
       "             ('pricey', 359),\n",
       "             ('larry', 12),\n",
       "             ('given', 405),\n",
       "             ('sort', 226),\n",
       "             ('joke', 43),\n",
       "             ('blown', 21),\n",
       "             ('away', 1131),\n",
       "             ('washington', 12),\n",
       "             ('state', 103),\n",
       "             ('burned', 59),\n",
       "             ('terrific', 178),\n",
       "             ('continue', 537),\n",
       "             ('unique', 231),\n",
       "             ('hidden', 36),\n",
       "             ('valley', 56),\n",
       "             ('discount', 130),\n",
       "             ('top', 907),\n",
       "             ('intact', 80),\n",
       "             ('reviewers', 233),\n",
       "             ('fruity', 190),\n",
       "             ('brewed', 236),\n",
       "             ('largest', 31),\n",
       "             ('setting', 127),\n",
       "             ('finish', 343),\n",
       "             ('stuck', 254),\n",
       "             ('fridge', 211),\n",
       "             ('next', 845),\n",
       "             ('iced', 507),\n",
       "             ('spilled', 36),\n",
       "             ('onto', 85),\n",
       "             ('others', 940),\n",
       "             ('spent', 130),\n",
       "             ('want', 2085),\n",
       "             ('liked', 1034),\n",
       "             ('creamer', 213),\n",
       "             ('see', 1321),\n",
       "             ('lot', 2084),\n",
       "             ('compared', 500),\n",
       "             ('health', 812),\n",
       "             ('value', 828),\n",
       "             ('suggest', 204),\n",
       "             ('sealed', 269),\n",
       "             ('jar', 438),\n",
       "             ('snack', 2486),\n",
       "             ('dish', 341),\n",
       "             ('absorbed', 13),\n",
       "             ('humidity', 7),\n",
       "             ('lost', 209),\n",
       "             ('crunchiness', 23),\n",
       "             ('otherwise', 296),\n",
       "             ('creamy', 453),\n",
       "             ('promised', 92),\n",
       "             ('personally', 233),\n",
       "             ('shipper', 48),\n",
       "             ('prompt', 106),\n",
       "             ('delivery', 670),\n",
       "             ('hardly', 125),\n",
       "             ('review', 640),\n",
       "             ('amazingly', 76),\n",
       "             ('pepperidge', 9),\n",
       "             ('farm', 52),\n",
       "             ('ones', 1057),\n",
       "             ('though', 1692),\n",
       "             ('variations', 17),\n",
       "             ('nearly', 260),\n",
       "             ('batch', 353),\n",
       "             ('nu', 2),\n",
       "             ('mug', 113),\n",
       "             ('nana', 42),\n",
       "             ('apbt', 2),\n",
       "             ('retriever', 55),\n",
       "             ('mix', 2573),\n",
       "             ('tiny', 327),\n",
       "             ('crunchies', 12),\n",
       "             ('break', 373),\n",
       "             ('gets', 610),\n",
       "             ('pretty', 1627),\n",
       "             ('messy', 142),\n",
       "             ('baby', 789),\n",
       "             ('zuke', 71),\n",
       "             ('mini', 211),\n",
       "             ('naturals', 40),\n",
       "             ('soft', 816),\n",
       "             ('mushy', 127),\n",
       "             ('greasy', 195),\n",
       "             ('puppy', 399),\n",
       "             ('salmon', 284),\n",
       "             ('goes', 645),\n",
       "             ('crazy', 366),\n",
       "             ('recommended', 920),\n",
       "             ('delighted', 163),\n",
       "             ('lightly', 116),\n",
       "             ('dusted', 19),\n",
       "             ('type', 626),\n",
       "             ('serve', 321),\n",
       "             ('party', 274),\n",
       "             ('elegant', 20),\n",
       "             ('packages', 484),\n",
       "             ('signature', 29),\n",
       "             ('codes', 3),\n",
       "             ('discs', 38),\n",
       "             ('defective', 22),\n",
       "             ('grounds', 111),\n",
       "             ('ended', 240),\n",
       "             ('bottom', 324),\n",
       "             ('spewed', 1),\n",
       "             ('coffeemaker', 24),\n",
       "             ('notified', 23),\n",
       "             ('gevalia', 63),\n",
       "             ('ago', 752),\n",
       "             ('offered', 212),\n",
       "             ('explanation', 10),\n",
       "             ('solution', 87),\n",
       "             ('disc', 24),\n",
       "             ('frustrating', 18),\n",
       "             ('paying', 227),\n",
       "             ('maxwell', 53),\n",
       "             ('seattle', 50),\n",
       "             ('definately', 177),\n",
       "             ('superb', 109),\n",
       "             ('aside', 56),\n",
       "             ('apple', 494),\n",
       "             ('mostly', 254),\n",
       "             ('blech', 13),\n",
       "             ('consistency', 412),\n",
       "             ('squashes', 1),\n",
       "             ('pasty', 18),\n",
       "             ('peas', 118),\n",
       "             ('powder', 630),\n",
       "             ('using', 1684),\n",
       "             ('sneak', 29),\n",
       "             ('nutrition', 248),\n",
       "             ('dishes', 260),\n",
       "             ('getting', 1201),\n",
       "             ('launching', 2),\n",
       "             ('idea', 433),\n",
       "             ('work', 1566),\n",
       "             ('crap', 83),\n",
       "             ('five', 351),\n",
       "             ('mine', 418),\n",
       "             ('texas', 69),\n",
       "             ('pete', 11),\n",
       "             ('hot', 2432),\n",
       "             ('sauce', 1496),\n",
       "             ('doctor', 85),\n",
       "             ('carrots', 71),\n",
       "             ('onion', 169),\n",
       "             ('parmesan', 47),\n",
       "             ('cheese', 1119),\n",
       "             ('cooked', 322),\n",
       "             ('leftover', 50),\n",
       "             ('chicken', 1313),\n",
       "             ('length', 58),\n",
       "             ('minsunder', 1),\n",
       "             ('cabinet', 80),\n",
       "             ('clearance', 9),\n",
       "             ('keurigkeurig', 1),\n",
       "             ('pod', 148),\n",
       "             ('drawer', 77),\n",
       "             ('holder', 38),\n",
       "             ('capacity', 5),\n",
       "             ('kitchen', 257),\n",
       "             ('sitting', 214),\n",
       "             ('sturdy', 78),\n",
       "             ('adds', 301),\n",
       "             ('atmosphere', 3),\n",
       "             ('allow', 92),\n",
       "             ('choose', 136),\n",
       "             ('art', 13),\n",
       "             ('function', 18),\n",
       "             ('glad', 948),\n",
       "             ('kosugai', 1),\n",
       "             ('incredibly', 154),\n",
       "             ('true', 363),\n",
       "             ('life', 491),\n",
       "             ('mainly', 66),\n",
       "             ('actual', 191),\n",
       "             ('main', 193),\n",
       "             ('ingredient', 363),\n",
       "             ('anything', 1350),\n",
       "             ('leathers', 6),\n",
       "             ('closest', 103),\n",
       "             ('thing', 1493),\n",
       "             ('terms', 72),\n",
       "             ('juiciness', 1),\n",
       "             ('particularly', 171),\n",
       "             ('mangosteen', 12),\n",
       "             ('lychee', 2),\n",
       "             ('kiwi', 125),\n",
       "             ('strawberry', 324),\n",
       "             ('thinking', 279),\n",
       "             ('meat', 607),\n",
       "             ('jelly', 247),\n",
       "             ('help', 693),\n",
       "             ('imagine', 204),\n",
       "             ('cook', 630),\n",
       "             ('thick', 367),\n",
       "             ('carve', 2),\n",
       "             ('sprinkle', 132),\n",
       "             ('side', 569),\n",
       "             ('pop', 532),\n",
       "             ('mouth', 666),\n",
       "             ('experimented', 14),\n",
       "             ('finishing', 60),\n",
       "             ('salts', 55),\n",
       "             ('starting', 110),\n",
       "             ('point', 276),\n",
       "             ('akitas', 1),\n",
       "             ('wait', 427),\n",
       "             ('stools', 37),\n",
       "             ('brew', 511),\n",
       "             ('six', 309),\n",
       "             ('gallon', 93),\n",
       "             ('agave', 140),\n",
       "             ('nectar', 88),\n",
       "             ('undertones', 16),\n",
       "             ('bright', 86),\n",
       "             ('everyday', 300),\n",
       "             ('economical', 107),\n",
       "             ('temp', 24),\n",
       "             ('processed', 150),\n",
       "             ('raw', 354),\n",
       "             ('must', 856),\n",
       "             ('mean', 178),\n",
       "             ('closer', 69),\n",
       "             ('pasteurized', 9),\n",
       "             ('honey', 938),\n",
       "             ('ails', 1),\n",
       "             ('malt', 35),\n",
       "             ('powders', 34),\n",
       "             ('looked', 504),\n",
       "             ('resulting', 28),\n",
       "             ('many', 2413),\n",
       "             ('carbs', 264),\n",
       "             ('diabetic', 154),\n",
       "             ('craving', 272),\n",
       "             ('frappe', 7),\n",
       "             ('frappes', 4),\n",
       "             ('drinks', 630),\n",
       "             ('carb', 331),\n",
       "             ('choice', 572),\n",
       "             ('tasting', 1718),\n",
       "             ('watermelon', 51),\n",
       "             ('sparkling', 93),\n",
       "             ('mixed', 569),\n",
       "             ('cranberry', 82),\n",
       "             ('juice', 920),\n",
       "             ('cocktail', 37),\n",
       "             ('grams', 241),\n",
       "             ('hi', 42),\n",
       "             ('cereal', 1322),\n",
       "             ('coupon', 46),\n",
       "             ('frozen', 195),\n",
       "             ('meals', 298),\n",
       "             ('boxes', 989),\n",
       "             ('plus', 801),\n",
       "             ('anyhow', 26),\n",
       "             ('ira', 1),\n",
       "             ...])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "src_tokenizer.word_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "inappropriate-horror",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "단어 집합(vocabulary)의 크기 : 31949\n",
      "등장 빈도가 6번 이하인 희귀 단어의 수: 23692\n",
      "단어 집합에서 희귀 단어를 제외시킬 경우의 단어 집합의 크기 8257\n",
      "단어 집합에서 희귀 단어의 비율: 74.1556856239632\n",
      "전체 등장 빈도에서 희귀 단어 등장 빈도 비율: 3.380007414748107\n"
     ]
    }
   ],
   "source": [
    "threshold = 7\n",
    "total_cnt = len(src_tokenizer.word_index) # 단어의 수\n",
    "rare_cnt = 0 # 등장 빈도수가 threshold보다 작은 단어의 개수를 카운트\n",
    "total_freq = 0 # 훈련 데이터의 전체 단어 빈도수 총 합\n",
    "rare_freq = 0 # 등장 빈도수가 threshold보다 작은 단어의 등장 빈도수의 총 합\n",
    "\n",
    "# 단어와 빈도수의 쌍(pair)을 key와 value로 받는다.\n",
    "for key, value in src_tokenizer.word_counts.items():\n",
    "    total_freq = total_freq + value\n",
    "\n",
    "    # 단어의 등장 빈도수가 threshold보다 작으면\n",
    "    if(value < threshold):\n",
    "        rare_cnt = rare_cnt + 1\n",
    "        rare_freq = rare_freq + value\n",
    "\n",
    "print('단어 집합(vocabulary)의 크기 :', total_cnt)\n",
    "print('등장 빈도가 %s번 이하인 희귀 단어의 수: %s'%(threshold - 1, rare_cnt))\n",
    "print('단어 집합에서 희귀 단어를 제외시킬 경우의 단어 집합의 크기 %s'%(total_cnt - rare_cnt))\n",
    "print(\"단어 집합에서 희귀 단어의 비율:\", (rare_cnt / total_cnt)*100)\n",
    "print(\"전체 등장 빈도에서 희귀 단어 등장 빈도 비율:\", (rare_freq / total_freq)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "outdoor-narrow",
   "metadata": {},
   "source": [
    "> 희한하게도 전체 단어의 약 75%가 등장 빈도에서는 약 3.3%만 차지하고 있다...\n",
    "\n",
    "> 예상하기로 이렇게 많은 단어를 제외하고 훈련시키면,  \n",
    "> - 장점은 훈련이 빠르게 진행되는 것이고  \n",
    "> - 단점은 그만큼 많은 단어의 풍부함(어휘력)이 없어질 것이다.  \n",
    "> - 하지만, 실제로 사용되는 횟수는 3%에 불과하다고 하니 없어도 무방할 듯하다..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "essential-shipping",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=3\n"
     ]
    }
   ],
   "source": [
    "# 어림잡아 8000개를 사용하는 단어로 잡는다\n",
    "src_vocab = 8000\n",
    "src_tokenizer = Tokenizer(num_words=src_vocab) # 단어 집합의 크기를 8,000으로 제한\n",
    "src_tokenizer.fit_on_texts(encoder_input_train) # 단어 집합 재생성\n",
    "print('=3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "portuguese-honor",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(15703, 'like'),\n",
       " (14861, 'good'),\n",
       " (14407, 'great'),\n",
       " (11624, 'taste'),\n",
       " (10814, 'product')]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted([(v, k) for k, v in src_tokenizer.word_counts.items()], reverse=True)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "exclusive-charlotte",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[21, 5, 4284, 562, 519, 274, 1092, 831, 993, 150, 2230, 278, 21, 2, 2340, 793, 249, 89, 3974, 79, 1593, 1459, 186, 78, 980, 201, 1321, 821, 3528, 297, 1465], [3194, 242, 7, 926, 245, 89, 67, 73, 145, 247, 4501, 1726, 306, 907, 3729, 6, 19, 7183, 208, 199, 72, 36, 19, 1887, 948, 1957, 5, 27], [1020, 5, 54, 73, 2492, 29, 351, 301, 3304, 458, 7, 4390, 376, 376, 39, 814, 4, 9, 434, 351, 301, 18, 2755, 4, 27, 148, 191, 29, 305]]\n"
     ]
    }
   ],
   "source": [
    "# 텍스트 시퀀스를 정수 시퀀스로 변환\n",
    "encoder_input_train = src_tokenizer.texts_to_sequences(encoder_input_train) \n",
    "encoder_input_test = src_tokenizer.texts_to_sequences(encoder_input_test)\n",
    "\n",
    "# 잘 진행되었는지 샘플 출력\n",
    "print(encoder_input_train[:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "creative-spelling",
   "metadata": {},
   "source": [
    "#### Summary에도 동일하게 수행"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "generic-brooklyn",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=3\n"
     ]
    }
   ],
   "source": [
    "tar_tokenizer = Tokenizer()\n",
    "tar_tokenizer.fit_on_texts(decoder_input_train)\n",
    "print('=3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "copyrighted-tiger",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['sostoken gluten free versatile and tastes good',\n",
       "       'sostoken arrived quickly and it was fresh',\n",
       "       'sostoken awesome cocoa'], dtype=object)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoder_input_train[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cathedral-terminal",
   "metadata": {},
   "source": [
    "> 위에처럼 단어 사전을 만들면서 부여된 정수는 `word_index`를 통해 확인 가능"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "impaired-birthday",
   "metadata": {},
   "source": [
    "> Summary도 빈도수를 기준으로 수치가 낮은 단어들은 걸러내기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "acquired-saturn",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "단어 집합(vocabulary)의 크기 : 10515\n",
      "등장 빈도가 5번 이하인 희귀 단어의 수: 8138\n",
      "단어 집합에서 희귀 단어를 제외시킬 경우의 단어 집합의 크기 2377\n",
      "단어 집합에서 희귀 단어의 비율: 77.39419876367096\n",
      "전체 등장 빈도에서 희귀 단어 등장 빈도 비율: 5.907358356482977\n"
     ]
    }
   ],
   "source": [
    "threshold = 6\n",
    "total_cnt = len(tar_tokenizer.word_index) # 단어의 수\n",
    "rare_cnt = 0 # 등장 빈도수가 threshold보다 작은 단어의 개수를 카운트\n",
    "total_freq = 0 # 훈련 데이터의 전체 단어 빈도수 총 합\n",
    "rare_freq = 0 # 등장 빈도수가 threshold보다 작은 단어의 등장 빈도수의 총 합\n",
    "\n",
    "# 단어와 빈도수의 쌍(pair)을 key와 value로 받는다.\n",
    "for key, value in tar_tokenizer.word_counts.items():\n",
    "    total_freq = total_freq + value\n",
    "\n",
    "    # 단어의 등장 빈도수가 threshold보다 작으면\n",
    "    if(value < threshold):\n",
    "        rare_cnt = rare_cnt + 1\n",
    "        rare_freq = rare_freq + value\n",
    "\n",
    "print('단어 집합(vocabulary)의 크기 :', total_cnt)\n",
    "print('등장 빈도가 %s번 이하인 희귀 단어의 수: %s'%(threshold - 1, rare_cnt))\n",
    "print('단어 집합에서 희귀 단어를 제외시킬 경우의 단어 집합의 크기 %s'%(total_cnt - rare_cnt))\n",
    "print(\"단어 집합에서 희귀 단어의 비율:\", (rare_cnt / total_cnt)*100)\n",
    "print(\"전체 등장 빈도에서 희귀 단어 등장 빈도 비율:\", (rare_freq / total_freq)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "passing-baseline",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input\n",
      "input  [[1, 81, 53, 667, 10, 59, 4], [1, 405, 820, 10, 11, 110, 137], [1, 41, 189], [1, 9, 15, 14], [1, 27, 1669]]\n",
      "target\n",
      "decoder  [[81, 53, 667, 10, 59, 4, 2], [405, 820, 10, 11, 110, 137, 2], [41, 189, 2], [9, 15, 14, 2], [27, 1669, 2]]\n"
     ]
    }
   ],
   "source": [
    "tar_vocab = 2000\n",
    "tar_tokenizer = Tokenizer(num_words=tar_vocab) \n",
    "tar_tokenizer.fit_on_texts(decoder_input_train)\n",
    "tar_tokenizer.fit_on_texts(decoder_target_train)\n",
    "\n",
    "# 텍스트 시퀀스를 정수 시퀀스로 변환\n",
    "decoder_input_train = tar_tokenizer.texts_to_sequences(decoder_input_train) \n",
    "decoder_target_train = tar_tokenizer.texts_to_sequences(decoder_target_train)\n",
    "decoder_input_test = tar_tokenizer.texts_to_sequences(decoder_input_test)\n",
    "decoder_target_test = tar_tokenizer.texts_to_sequences(decoder_target_test)\n",
    "\n",
    "# 잘 변환되었는지 확인\n",
    "print('input')\n",
    "print('input ',decoder_input_train[:5])\n",
    "print('target')\n",
    "print('decoder ',decoder_target_train[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "speaking-tragedy",
   "metadata": {},
   "source": [
    "> 근데 이렇게 num_words를 설정해서 자꾸 단어들을 날리면, 단어 사전에 없는 단어를 가진 원본 텍스트는 정수 인코딩 후에 empty로 되어 있을 수 있다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "occasional-mistress",
   "metadata": {},
   "source": [
    "> 그렇게 empty가 된 건 그래도 빈도수가 제일 높은 시작, 종료 토큰은 있을 테니까 길이가 1이 된다.  \n",
    "> 그래서 길이가 1인 문장들을 또 걸러낸다"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prescription-pilot",
   "metadata": {},
   "source": [
    "> 이 때 encoder 부분도 확인하면 좋지만, 가정으로 깔고 가는 건, 원래 문장이 긴 것이다 보니 empty로 바뀐 건 없을 것이란 거다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "constant-cooling",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "삭제할 훈련 데이터의 개수 : 1246\n",
      "삭제할 테스트 데이터의 개수 : 329\n",
      "훈련 데이터의 개수 : 51409\n",
      "훈련 레이블의 개수 : 51409\n",
      "테스트 데이터의 개수 : 12834\n",
      "테스트 레이블의 개수 : 12834\n"
     ]
    }
   ],
   "source": [
    "drop_train = [index for index, sentence in enumerate(decoder_input_train) if len(sentence) == 1]\n",
    "drop_test = [index for index, sentence in enumerate(decoder_input_test) if len(sentence) == 1]\n",
    "\n",
    "print('삭제할 훈련 데이터의 개수 :', len(drop_train))\n",
    "print('삭제할 테스트 데이터의 개수 :', len(drop_test))\n",
    "\n",
    "encoder_input_train = [sentence for index, sentence in enumerate(encoder_input_train) if index not in drop_train]\n",
    "decoder_input_train = [sentence for index, sentence in enumerate(decoder_input_train) if index not in drop_train]\n",
    "decoder_target_train = [sentence for index, sentence in enumerate(decoder_target_train) if index not in drop_train]\n",
    "\n",
    "encoder_input_test = [sentence for index, sentence in enumerate(encoder_input_test) if index not in drop_test]\n",
    "decoder_input_test = [sentence for index, sentence in enumerate(decoder_input_test) if index not in drop_test]\n",
    "decoder_target_test = [sentence for index, sentence in enumerate(decoder_target_test) if index not in drop_test]\n",
    "\n",
    "print('훈련 데이터의 개수 :', len(encoder_input_train))\n",
    "print('훈련 레이블의 개수 :', len(decoder_input_train))\n",
    "print('테스트 데이터의 개수 :', len(encoder_input_test))\n",
    "print('테스트 레이블의 개수 :', len(decoder_input_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "automatic-lodge",
   "metadata": {},
   "source": [
    "#### 패딩하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "outer-ballet",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=3\n"
     ]
    }
   ],
   "source": [
    "encoder_input_train = pad_sequences(encoder_input_train, maxlen=text_max_len, padding='post')\n",
    "encoder_input_test = pad_sequences(encoder_input_test, maxlen=text_max_len, padding='post')\n",
    "decoder_input_train = pad_sequences(decoder_input_train, maxlen=summary_max_len, padding='post')\n",
    "decoder_target_train = pad_sequences(decoder_target_train, maxlen=summary_max_len, padding='post')\n",
    "decoder_input_test = pad_sequences(decoder_input_test, maxlen=summary_max_len, padding='post')\n",
    "decoder_target_test = pad_sequences(decoder_target_test, maxlen=summary_max_len, padding='post')\n",
    "print('=3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "cooked-operation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  21,    5, 4284,  562,  519,  274, 1092,  831,  993,  150, 2230,\n",
       "         278,   21,    2, 2340,  793,  249,   89, 3974,   79, 1593, 1459,\n",
       "         186,   78,  980,  201, 1321,  821, 3528,  297, 1465,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0],\n",
       "       [3194,  242,    7,  926,  245,   89,   67,   73,  145,  247, 4501,\n",
       "        1726,  306,  907, 3729,    6,   19, 7183,  208,  199,   72,   36,\n",
       "          19, 1887,  948, 1957,    5,   27,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0]], dtype=int32)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder_input_train[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "accessible-garage",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51409, 50)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder_input_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "global-entry",
   "metadata": {},
   "source": [
    "## 모델 설계하기"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prescribed-jurisdiction",
   "metadata": {},
   "source": [
    "> Functional API 사용"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cross-woman",
   "metadata": {},
   "source": [
    "> 여기서 hidden_size는 LSTM에서 hidden_state의 뉴런 개수라고 이해하면 됨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "simple-volleyball",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n",
      "WARNING:tensorflow:Layer lstm_2 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.layers import Input, LSTM, Embedding, Dense, Concatenate, TimeDistributed\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "\n",
    "\n",
    "# 인코더 설계 시작\n",
    "embedding_dim = 128\n",
    "hidden_size = 256\n",
    "\n",
    "# 인코더\n",
    "encoder_inputs = Input(shape=(text_max_len,)) # DJ - 아마도 여기 text_max_len만 넣는 건 데이터 갯수를 모른다는 가정으로 feature만 넣은 듯하다.\n",
    "\n",
    "# 인코더의 임베딩 층\n",
    "enc_emb = Embedding(src_vocab, embedding_dim)(encoder_inputs) # DJ - 근데 임베딩 층에서는 (8000개, 원하는 벡터 차원수)를 명시해준다.\n",
    "\n",
    "# 인코더의 LSTM 1\n",
    "encoder_lstm1 = LSTM(hidden_size, return_sequences=True, return_state=True ,dropout = 0.4, recurrent_dropout = 0.4)\n",
    "encoder_output1, state_h1, state_c1 = encoder_lstm1(enc_emb)\n",
    "\n",
    "# 인코더의 LSTM 2\n",
    "encoder_lstm2 = LSTM(hidden_size, return_sequences=True, return_state=True, dropout=0.4, recurrent_dropout=0.4)\n",
    "encoder_output2, state_h2, state_c2 = encoder_lstm2(encoder_output1)\n",
    "\n",
    "# 인코더의 LSTM 3\n",
    "encoder_lstm3 = LSTM(hidden_size, return_state=True, return_sequences=True, dropout=0.4, recurrent_dropout=0.4)\n",
    "encoder_outputs, state_h, state_c= encoder_lstm3(encoder_output2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "meaningful-vessel",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm_3 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n"
     ]
    }
   ],
   "source": [
    "# 디코더 설계\n",
    "decoder_inputs = Input(shape=(None,))\n",
    "\n",
    "# 디코더의 임베딩 층\n",
    "dec_emb_layer = Embedding(tar_vocab, embedding_dim)\n",
    "dec_emb = dec_emb_layer(decoder_inputs)\n",
    "\n",
    "# 디코더의 LSTM\n",
    "decoder_lstm = LSTM(hidden_size, return_sequences=True, return_state=True, dropout=0.4, recurrent_dropout=0.2)\n",
    "decoder_outputs, _, _ = decoder_lstm(dec_emb, initial_state=[state_h, state_c])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "likely-replication",
   "metadata": {},
   "source": [
    "-----------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "aboriginal-mississippi",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KerasTensor: shape=(None, None) dtype=float32 (created by layer 'input_2')>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoder_inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "ready-identification",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.layers.embeddings.Embedding at 0x7fa7bacb7910>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec_emb_layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "leading-interstate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KerasTensor: shape=(None, None, 128) dtype=float32 (created by layer 'embedding_1')>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec_emb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "christian-recall",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.layers.recurrent_v2.LSTM at 0x7fa7bbbac990>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoder_lstm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "dress-contrary",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KerasTensor: shape=(None, None, 256) dtype=float32 (created by layer 'lstm_3')>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoder_outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "basic-level",
   "metadata": {},
   "source": [
    "-----------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "subsequent-cisco",
   "metadata": {},
   "source": [
    "> 출력층 첫번째 `Dense()`은 전체 target_vocab 숫자의 확률을 반환해야 하기 때문에 `뉴런 개수`로 그 값을 넣는다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "inappropriate-conversion",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 50)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding (Embedding)           (None, 50, 128)      1024000     input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lstm (LSTM)                     [(None, 50, 256), (N 394240      embedding[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "input_2 (InputLayer)            [(None, None)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "lstm_1 (LSTM)                   [(None, 50, 256), (N 525312      lstm[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "embedding_1 (Embedding)         (None, None, 128)    256000      input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lstm_2 (LSTM)                   [(None, 50, 256), (N 525312      lstm_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "lstm_3 (LSTM)                   [(None, None, 256),  394240      embedding_1[0][0]                \n",
      "                                                                 lstm_2[0][1]                     \n",
      "                                                                 lstm_2[0][2]                     \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, None, 2000)   514000      lstm_3[0][0]                     \n",
      "==================================================================================================\n",
      "Total params: 3,633,104\n",
      "Trainable params: 3,633,104\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 디코더의 출력층\n",
    "decoder_softmax_layer = Dense(tar_vocab, activation='softmax')\n",
    "decoder_softmax_outputs = decoder_softmax_layer(decoder_outputs) \n",
    "\n",
    "# 모델 정의\n",
    "model = Model([encoder_inputs, decoder_inputs], decoder_softmax_outputs)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "quiet-berlin",
   "metadata": {},
   "source": [
    "#### 위는 기본적인 LSTM이니, attention 매커니즘을 쓴 모델도 써보자"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "olive-directory",
   "metadata": {},
   "source": [
    "> 아래의 코드는 인코더의 hidden state들과 디코더의 hidden state들을 어텐션 함수의 입력으로 사용하고, 어텐션 함수가 리턴한 값을 예측 시에 디코더의 hidden state와 함께 활용하는 형태로 작동"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "balanced-dialogue",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 50)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding (Embedding)           (None, 50, 128)      1024000     input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lstm (LSTM)                     [(None, 50, 256), (N 394240      embedding[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "input_2 (InputLayer)            [(None, None)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "lstm_1 (LSTM)                   [(None, 50, 256), (N 525312      lstm[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "embedding_1 (Embedding)         (None, None, 128)    256000      input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lstm_2 (LSTM)                   [(None, 50, 256), (N 525312      lstm_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "lstm_3 (LSTM)                   [(None, None, 256),  394240      embedding_1[0][0]                \n",
      "                                                                 lstm_2[0][1]                     \n",
      "                                                                 lstm_2[0][2]                     \n",
      "__________________________________________________________________________________________________\n",
      "attention_layer (AdditiveAttent (None, None, 256)    256         lstm_3[0][0]                     \n",
      "                                                                 lstm_2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "concat_layer (Concatenate)      (None, None, 512)    0           lstm_3[0][0]                     \n",
      "                                                                 attention_layer[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, None, 2000)   1026000     concat_layer[0][0]               \n",
      "==================================================================================================\n",
      "Total params: 4,145,360\n",
      "Trainable params: 4,145,360\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.layers import AdditiveAttention\n",
    "\n",
    "# 어텐션 층(어텐션 함수)\n",
    "attn_layer = AdditiveAttention(name='attention_layer')\n",
    "\n",
    "# 인코더와 디코더의 모든 time step의 hidden state를 어텐션 층에 전달하고 결과를 리턴\n",
    "attn_out = attn_layer([decoder_outputs, encoder_outputs])\n",
    "\n",
    "\n",
    "# 어텐션의 결과와 디코더의 hidden state들을 연결\n",
    "decoder_concat_input = Concatenate(axis=-1, name='concat_layer')([decoder_outputs, attn_out])\n",
    "\n",
    "# 디코더의 출력층\n",
    "decoder_softmax_layer = Dense(tar_vocab, activation='softmax')\n",
    "decoder_softmax_outputs = decoder_softmax_layer(decoder_concat_input)\n",
    "\n",
    "# 모델 정의\n",
    "model = Model([encoder_inputs, decoder_inputs], decoder_softmax_outputs)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "upset-mattress",
   "metadata": {},
   "source": [
    "## 모델 훈련하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "distributed-doctrine",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "201/201 [==============================] - 124s 570ms/step - loss: 3.1242 - val_loss: 2.4109\n",
      "Epoch 2/50\n",
      "201/201 [==============================] - 114s 566ms/step - loss: 2.4021 - val_loss: 2.2703\n",
      "Epoch 3/50\n",
      "201/201 [==============================] - 114s 565ms/step - loss: 2.2528 - val_loss: 2.1498\n",
      "Epoch 4/50\n",
      "201/201 [==============================] - 114s 567ms/step - loss: 2.1304 - val_loss: 2.0590\n",
      "Epoch 5/50\n",
      "201/201 [==============================] - 114s 567ms/step - loss: 2.0188 - val_loss: 2.0054\n",
      "Epoch 6/50\n",
      "201/201 [==============================] - 114s 566ms/step - loss: 1.9495 - val_loss: 1.9615\n",
      "Epoch 7/50\n",
      "201/201 [==============================] - 114s 566ms/step - loss: 1.9042 - val_loss: 1.9373\n",
      "Epoch 8/50\n",
      "201/201 [==============================] - 114s 567ms/step - loss: 1.8537 - val_loss: 1.9109\n",
      "Epoch 9/50\n",
      "201/201 [==============================] - 114s 566ms/step - loss: 1.8001 - val_loss: 1.8914\n",
      "Epoch 10/50\n",
      "201/201 [==============================] - 114s 566ms/step - loss: 1.7753 - val_loss: 1.8810\n",
      "Epoch 11/50\n",
      "201/201 [==============================] - 114s 567ms/step - loss: 1.7433 - val_loss: 1.8672\n",
      "Epoch 12/50\n",
      "201/201 [==============================] - 114s 567ms/step - loss: 1.7087 - val_loss: 1.8593\n",
      "Epoch 13/50\n",
      "201/201 [==============================] - 114s 566ms/step - loss: 1.6758 - val_loss: 1.8541\n",
      "Epoch 14/50\n",
      "201/201 [==============================] - 114s 566ms/step - loss: 1.6489 - val_loss: 1.8531\n",
      "Epoch 15/50\n",
      "201/201 [==============================] - 114s 567ms/step - loss: 1.6274 - val_loss: 1.8465\n",
      "Epoch 16/50\n",
      "201/201 [==============================] - 114s 569ms/step - loss: 1.5967 - val_loss: 1.8471\n",
      "Epoch 17/50\n",
      "201/201 [==============================] - 114s 567ms/step - loss: 1.5798 - val_loss: 1.8465\n",
      "Epoch 00017: early stopping\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy')\n",
    "es = EarlyStopping(monitor='val_loss', patience=2, verbose=1)\n",
    "history = model.fit(x=[encoder_input_train, decoder_input_train], y=decoder_target_train, \\\n",
    "          validation_data=([encoder_input_test, decoder_input_test], decoder_target_test), \\\n",
    "          batch_size=256, callbacks=[es], epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "immediate-reunion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='train')\n",
    "plt.plot(history.history['val_loss'], label='test')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "expensive-class",
   "metadata": {},
   "source": [
    "## 인퍼런스 모델 구현하기"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cheap-fraud",
   "metadata": {},
   "source": [
    "> 테스트할 때는 정수 -> 단어로 구현되어야 하니까 그것에 필요한 것들을 준비한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "later-record",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=3\n"
     ]
    }
   ],
   "source": [
    "src_index_to_word = src_tokenizer.index_word # 원문 단어 집합에서 정수 -> 단어를 얻음\n",
    "tar_word_to_index = tar_tokenizer.word_index # 요약 단어 집합에서 단어 -> 정수를 얻음\n",
    "tar_index_to_word = tar_tokenizer.index_word # 요약 단어 집합에서 정수 -> 단어를 얻음\n",
    "\n",
    "print('=3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "sharp-postcard",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'like',\n",
       " 2: 'good',\n",
       " 3: 'great',\n",
       " 4: 'taste',\n",
       " 5: 'product',\n",
       " 6: 'love',\n",
       " 7: 'one',\n",
       " 8: 'coffee',\n",
       " 9: 'flavor',\n",
       " 10: 'would',\n",
       " 11: 'tea',\n",
       " 12: 'really',\n",
       " 13: 'get',\n",
       " 14: 'amazon',\n",
       " 15: 'price',\n",
       " 16: 'best',\n",
       " 17: 'buy',\n",
       " 18: 'much',\n",
       " 19: 'food',\n",
       " 20: 'time',\n",
       " 21: 'use',\n",
       " 22: 'little',\n",
       " 23: 'find',\n",
       " 24: 'tried',\n",
       " 25: 'better',\n",
       " 26: 'also',\n",
       " 27: 'well',\n",
       " 28: 'make',\n",
       " 29: 'chocolate',\n",
       " 30: 'try',\n",
       " 31: 'eat',\n",
       " 32: 'dog',\n",
       " 33: 'even',\n",
       " 34: 'bought',\n",
       " 35: 'delicious',\n",
       " 36: 'found',\n",
       " 37: 'sweet',\n",
       " 38: 'order',\n",
       " 39: 'could',\n",
       " 40: 'cup',\n",
       " 41: 'recommend',\n",
       " 42: 'drink',\n",
       " 43: 'tastes',\n",
       " 44: 'used',\n",
       " 45: 'loves',\n",
       " 46: 'bag',\n",
       " 47: 'sugar',\n",
       " 48: 'favorite',\n",
       " 49: 'cannot',\n",
       " 50: 'first',\n",
       " 51: 'store',\n",
       " 52: 'free',\n",
       " 53: 'nice',\n",
       " 54: 'made',\n",
       " 55: 'way',\n",
       " 56: 'box',\n",
       " 57: 'go',\n",
       " 58: 'got',\n",
       " 59: 'perfect',\n",
       " 60: 'mix',\n",
       " 61: 'dogs',\n",
       " 62: 'water',\n",
       " 63: 'since',\n",
       " 64: 'easy',\n",
       " 65: 'day',\n",
       " 66: 'think',\n",
       " 67: 'ordered',\n",
       " 68: 'snack',\n",
       " 69: 'bit',\n",
       " 70: 'give',\n",
       " 71: 'hot',\n",
       " 72: 'flavors',\n",
       " 73: 'two',\n",
       " 74: 'many',\n",
       " 75: 'still',\n",
       " 76: 'never',\n",
       " 77: 'ever',\n",
       " 78: 'makes',\n",
       " 79: 'every',\n",
       " 80: 'stuff',\n",
       " 81: 'treats',\n",
       " 82: 'quality',\n",
       " 83: 'always',\n",
       " 84: 'without',\n",
       " 85: 'brand',\n",
       " 86: 'right',\n",
       " 87: 'know',\n",
       " 88: 'healthy',\n",
       " 89: 'fresh',\n",
       " 90: 'add',\n",
       " 91: 'tasty',\n",
       " 92: 'definitely',\n",
       " 93: 'hard',\n",
       " 94: 'want',\n",
       " 95: 'lot',\n",
       " 96: 'years',\n",
       " 97: 'keep',\n",
       " 98: 'something',\n",
       " 99: 'strong',\n",
       " 100: 'buying',\n",
       " 101: 'happy',\n",
       " 102: 'small',\n",
       " 103: 'enjoy',\n",
       " 104: 'wonderful',\n",
       " 105: 'treat',\n",
       " 106: 'enough',\n",
       " 107: 'bad',\n",
       " 108: 'excellent',\n",
       " 109: 'local',\n",
       " 110: 'old',\n",
       " 111: 'chips',\n",
       " 112: 'less',\n",
       " 113: 'milk',\n",
       " 114: 'say',\n",
       " 115: 'different',\n",
       " 116: 'highly',\n",
       " 117: 'purchase',\n",
       " 118: 'shipping',\n",
       " 119: 'need',\n",
       " 120: 'however',\n",
       " 121: 'organic',\n",
       " 122: 'sure',\n",
       " 123: 'regular',\n",
       " 124: 'far',\n",
       " 125: 'size',\n",
       " 126: 'thought',\n",
       " 127: 'eating',\n",
       " 128: 'salt',\n",
       " 129: 'tasting',\n",
       " 130: 'long',\n",
       " 131: 'products',\n",
       " 132: 'tasted',\n",
       " 133: 'stores',\n",
       " 134: 'put',\n",
       " 135: 'though',\n",
       " 136: 'using',\n",
       " 137: 'looking',\n",
       " 138: 'back',\n",
       " 139: 'purchased',\n",
       " 140: 'whole',\n",
       " 141: 'pack',\n",
       " 142: 'bars',\n",
       " 143: 'pretty',\n",
       " 144: 'cups',\n",
       " 145: 'bags',\n",
       " 146: 'loved',\n",
       " 147: 'work',\n",
       " 148: 'worth',\n",
       " 149: 'high',\n",
       " 150: 'gluten',\n",
       " 151: 'low',\n",
       " 152: 'cookies',\n",
       " 153: 'texture',\n",
       " 154: 'package',\n",
       " 155: 'family',\n",
       " 156: 'big',\n",
       " 157: 'ingredients',\n",
       " 158: 'sauce',\n",
       " 159: 'arrived',\n",
       " 160: 'thing',\n",
       " 161: 'received',\n",
       " 162: 'quite',\n",
       " 163: 'green',\n",
       " 164: 'grocery',\n",
       " 165: 'last',\n",
       " 166: 'popcorn',\n",
       " 167: 'rice',\n",
       " 168: 'going',\n",
       " 169: 'candy',\n",
       " 170: 'year',\n",
       " 171: 'take',\n",
       " 172: 'item',\n",
       " 173: 'oil',\n",
       " 174: 'real',\n",
       " 175: 'actually',\n",
       " 176: 'came',\n",
       " 177: 'expensive',\n",
       " 178: 'cat',\n",
       " 179: 'anything',\n",
       " 180: 'almost',\n",
       " 181: 'calories',\n",
       " 182: 'dark',\n",
       " 183: 'cereal',\n",
       " 184: 'see',\n",
       " 185: 'chicken',\n",
       " 186: 'husband',\n",
       " 187: 'feel',\n",
       " 188: 'natural',\n",
       " 189: 'new',\n",
       " 190: 'butter',\n",
       " 191: 'money',\n",
       " 192: 'another',\n",
       " 193: 'full',\n",
       " 194: 'kids',\n",
       " 195: 'trying',\n",
       " 196: 'blend',\n",
       " 197: 'people',\n",
       " 198: 'bar',\n",
       " 199: 'brands',\n",
       " 200: 'getting',\n",
       " 201: 'fruit',\n",
       " 202: 'smell',\n",
       " 203: 'morning',\n",
       " 204: 'absolutely',\n",
       " 205: 'coconut',\n",
       " 206: 'disappointed',\n",
       " 207: 'home',\n",
       " 208: 'several',\n",
       " 209: 'amount',\n",
       " 210: 'fast',\n",
       " 211: 'added',\n",
       " 212: 'around',\n",
       " 213: 'half',\n",
       " 214: 'away',\n",
       " 215: 'wish',\n",
       " 216: 'cheese',\n",
       " 217: 'diet',\n",
       " 218: 'available',\n",
       " 219: 'bitter',\n",
       " 220: 'oz',\n",
       " 221: 'us',\n",
       " 222: 'deal',\n",
       " 223: 'kind',\n",
       " 224: 'able',\n",
       " 225: 'dry',\n",
       " 226: 'fat',\n",
       " 227: 'usually',\n",
       " 228: 'foods',\n",
       " 229: 'ones',\n",
       " 230: 'cats',\n",
       " 231: 'flavored',\n",
       " 232: 'probably',\n",
       " 233: 'smooth',\n",
       " 234: 'nothing',\n",
       " 235: 'come',\n",
       " 236: 'may',\n",
       " 237: 'case',\n",
       " 238: 'works',\n",
       " 239: 'light',\n",
       " 240: 'liked',\n",
       " 241: 'per',\n",
       " 242: 'ordering',\n",
       " 243: 'days',\n",
       " 244: 'company',\n",
       " 245: 'said',\n",
       " 246: 'breakfast',\n",
       " 247: 'problem',\n",
       " 248: 'gift',\n",
       " 249: 'bread',\n",
       " 250: 'cheaper',\n",
       " 251: 'save',\n",
       " 252: 'quick',\n",
       " 253: 'things',\n",
       " 254: 'boxes',\n",
       " 255: 'months',\n",
       " 256: 'seems',\n",
       " 257: 'making',\n",
       " 258: 'quickly',\n",
       " 259: 'variety',\n",
       " 260: 'black',\n",
       " 261: 'fine',\n",
       " 262: 'especially',\n",
       " 263: 'large',\n",
       " 264: 'peanut',\n",
       " 265: 'packaging',\n",
       " 266: 'reviews',\n",
       " 267: 'pasta',\n",
       " 268: 'glad',\n",
       " 269: 'bottle',\n",
       " 270: 'rich',\n",
       " 271: 'others',\n",
       " 272: 'honey',\n",
       " 273: 'instead',\n",
       " 274: 'son',\n",
       " 275: 'three',\n",
       " 276: 'recommended',\n",
       " 277: 'juice',\n",
       " 278: 'house',\n",
       " 279: 'gave',\n",
       " 280: 'comes',\n",
       " 281: 'drinking',\n",
       " 282: 'protein',\n",
       " 283: 'top',\n",
       " 284: 'roast',\n",
       " 285: 'prefer',\n",
       " 286: 'energy',\n",
       " 287: 'thank',\n",
       " 288: 'spicy',\n",
       " 289: 'look',\n",
       " 290: 'syrup',\n",
       " 291: 'amazing',\n",
       " 292: 'anyone',\n",
       " 293: 'times',\n",
       " 294: 'everyone',\n",
       " 295: 'must',\n",
       " 296: 'month',\n",
       " 297: 'thanks',\n",
       " 298: 'extra',\n",
       " 299: 'cans',\n",
       " 300: 'meal',\n",
       " 301: 'cream',\n",
       " 302: 'white',\n",
       " 303: 'yummy',\n",
       " 304: 'next',\n",
       " 305: 'fan',\n",
       " 306: 'daughter',\n",
       " 307: 'maybe',\n",
       " 308: 'teeth',\n",
       " 309: 'pieces',\n",
       " 310: 'seem',\n",
       " 311: 'vanilla',\n",
       " 312: 'value',\n",
       " 313: 'awesome',\n",
       " 314: 'wanted',\n",
       " 315: 'fact',\n",
       " 316: 'likes',\n",
       " 317: 'might',\n",
       " 318: 'soft',\n",
       " 319: 'everything',\n",
       " 320: 'salty',\n",
       " 321: 'keurig',\n",
       " 322: 'stars',\n",
       " 323: 'health',\n",
       " 324: 'either',\n",
       " 325: 'else',\n",
       " 326: 'friends',\n",
       " 327: 'nuts',\n",
       " 328: 'teas',\n",
       " 329: 'crunchy',\n",
       " 330: 'cost',\n",
       " 331: 'plus',\n",
       " 332: 'least',\n",
       " 333: 'longer',\n",
       " 334: 'baby',\n",
       " 335: 'enjoyed',\n",
       " 336: 'super',\n",
       " 337: 'yet',\n",
       " 338: 'bold',\n",
       " 339: 'couple',\n",
       " 340: 'ginger',\n",
       " 341: 'went',\n",
       " 342: 'online',\n",
       " 343: 'beans',\n",
       " 344: 'ago',\n",
       " 345: 'open',\n",
       " 346: 'soup',\n",
       " 347: 'market',\n",
       " 348: 'pleased',\n",
       " 349: 'plastic',\n",
       " 350: 'decided',\n",
       " 351: 'ice',\n",
       " 352: 'took',\n",
       " 353: 'ok',\n",
       " 354: 'chew',\n",
       " 355: 'help',\n",
       " 356: 'started',\n",
       " 357: 'corn',\n",
       " 358: 'expected',\n",
       " 359: 'alternative',\n",
       " 360: 'noodles',\n",
       " 361: 'flavorful',\n",
       " 362: 'delivery',\n",
       " 363: 'although',\n",
       " 364: 'snacks',\n",
       " 365: 'says',\n",
       " 366: 'week',\n",
       " 367: 'mouth',\n",
       " 368: 'rather',\n",
       " 369: 'machine',\n",
       " 370: 'jerky',\n",
       " 371: 'minutes',\n",
       " 372: 'service',\n",
       " 373: 'pepper',\n",
       " 374: 'wheat',\n",
       " 375: 'ounce',\n",
       " 376: 'cocoa',\n",
       " 377: 'cold',\n",
       " 378: 'goes',\n",
       " 379: 'crackers',\n",
       " 380: 'read',\n",
       " 381: 'review',\n",
       " 382: 'let',\n",
       " 383: 'beef',\n",
       " 384: 'lemon',\n",
       " 385: 'second',\n",
       " 386: 'plain',\n",
       " 387: 'powder',\n",
       " 388: 'cook',\n",
       " 389: 'drinks',\n",
       " 390: 'soda',\n",
       " 391: 'type',\n",
       " 392: 'cookie',\n",
       " 393: 'weight',\n",
       " 394: 'opened',\n",
       " 395: 'decaf',\n",
       " 396: 'special',\n",
       " 397: 'gets',\n",
       " 398: 'surprised',\n",
       " 399: 'meat',\n",
       " 400: 'sometimes',\n",
       " 401: 'wife',\n",
       " 402: 'version',\n",
       " 403: 'cooking',\n",
       " 404: 'french',\n",
       " 405: 'shipped',\n",
       " 406: 'aroma',\n",
       " 407: 'red',\n",
       " 408: 'exactly',\n",
       " 409: 'dried',\n",
       " 410: 'part',\n",
       " 411: 'convenient',\n",
       " 412: 'fantastic',\n",
       " 413: 'friend',\n",
       " 414: 'smaller',\n",
       " 415: 'tell',\n",
       " 416: 'place',\n",
       " 417: 'cake',\n",
       " 418: 'clean',\n",
       " 419: 'picky',\n",
       " 420: 'choice',\n",
       " 421: 'hand',\n",
       " 422: 'easily',\n",
       " 423: 'side',\n",
       " 424: 'mixed',\n",
       " 425: 'believe',\n",
       " 426: 'packaged',\n",
       " 427: 'wrong',\n",
       " 428: 'reason',\n",
       " 429: 'smells',\n",
       " 430: 'problems',\n",
       " 431: 'orange',\n",
       " 432: 'artificial',\n",
       " 433: 'guess',\n",
       " 434: 'difference',\n",
       " 435: 'refreshing',\n",
       " 436: 'giving',\n",
       " 437: 'care',\n",
       " 438: 'subscribe',\n",
       " 439: 'coffees',\n",
       " 440: 'slightly',\n",
       " 441: 'bulk',\n",
       " 442: 'packs',\n",
       " 443: 'live',\n",
       " 444: 'left',\n",
       " 445: 'seeds',\n",
       " 446: 'aftertaste',\n",
       " 447: 'continue',\n",
       " 448: 'cinnamon',\n",
       " 449: 'serving',\n",
       " 450: 'recently',\n",
       " 451: 'christmas',\n",
       " 452: 'pop',\n",
       " 453: 'container',\n",
       " 454: 'chewy',\n",
       " 455: 'cut',\n",
       " 456: 'mild',\n",
       " 457: 'starbucks',\n",
       " 458: 'recipe',\n",
       " 459: 'etc',\n",
       " 460: 'spice',\n",
       " 461: 'bite',\n",
       " 462: 'potato',\n",
       " 463: 'oatmeal',\n",
       " 464: 'delivered',\n",
       " 465: 'line',\n",
       " 466: 'brown',\n",
       " 467: 'often',\n",
       " 468: 'sent',\n",
       " 469: 'brew',\n",
       " 470: 'stomach',\n",
       " 471: 'lots',\n",
       " 472: 'almonds',\n",
       " 473: 'iced',\n",
       " 474: 'needed',\n",
       " 475: 'looked',\n",
       " 476: 'bottles',\n",
       " 477: 'dinner',\n",
       " 478: 'pay',\n",
       " 479: 'looks',\n",
       " 480: 'compared',\n",
       " 481: 'huge',\n",
       " 482: 'fiber',\n",
       " 483: 'cherry',\n",
       " 484: 'past',\n",
       " 485: 'apple',\n",
       " 486: 'run',\n",
       " 487: 'ate',\n",
       " 488: 'calorie',\n",
       " 489: 'filling',\n",
       " 490: 'lunch',\n",
       " 491: 'life',\n",
       " 492: 'night',\n",
       " 493: 'end',\n",
       " 494: 'simply',\n",
       " 495: 'excited',\n",
       " 496: 'date',\n",
       " 497: 'close',\n",
       " 498: 'packages',\n",
       " 499: 'gives',\n",
       " 500: 'carry',\n",
       " 501: 'seller',\n",
       " 502: 'larger',\n",
       " 503: 'start',\n",
       " 504: 'granola',\n",
       " 505: 'chai',\n",
       " 506: 'weeks',\n",
       " 507: 'seasoning',\n",
       " 508: 'eaten',\n",
       " 509: 'stick',\n",
       " 510: 'original',\n",
       " 511: 'toy',\n",
       " 512: 'healthier',\n",
       " 513: 'items',\n",
       " 514: 'com',\n",
       " 515: 'unfortunately',\n",
       " 516: 'chip',\n",
       " 517: 'someone',\n",
       " 518: 'waste',\n",
       " 519: 'needs',\n",
       " 520: 'salad',\n",
       " 521: 'creamy',\n",
       " 522: 'helps',\n",
       " 523: 'saw',\n",
       " 524: 'condition',\n",
       " 525: 'hope',\n",
       " 526: 'soon',\n",
       " 527: 'similar',\n",
       " 528: 'canned',\n",
       " 529: 'broken',\n",
       " 530: 'gum',\n",
       " 531: 'color',\n",
       " 532: 'olive',\n",
       " 533: 'nut',\n",
       " 534: 'gone',\n",
       " 535: 'inside',\n",
       " 536: 'already',\n",
       " 537: 'purchasing',\n",
       " 538: 'overall',\n",
       " 539: 'feed',\n",
       " 540: 'extremely',\n",
       " 541: 'takes',\n",
       " 542: 'jar',\n",
       " 543: 'stock',\n",
       " 544: 'stale',\n",
       " 545: 'idea',\n",
       " 546: 'sweetness',\n",
       " 547: 'four',\n",
       " 548: 'crunch',\n",
       " 549: 'heat',\n",
       " 550: 'star',\n",
       " 551: 'pound',\n",
       " 552: 'weak',\n",
       " 553: 'packed',\n",
       " 554: 'wait',\n",
       " 555: 'finally',\n",
       " 556: 'single',\n",
       " 557: 'pumpkin',\n",
       " 558: 'keeps',\n",
       " 559: 'beat',\n",
       " 560: 'mine',\n",
       " 561: 'microwave',\n",
       " 562: 'baking',\n",
       " 563: 'soy',\n",
       " 564: 'fish',\n",
       " 565: 'change',\n",
       " 566: 'caffeine',\n",
       " 567: 'experience',\n",
       " 568: 'consistency',\n",
       " 569: 'course',\n",
       " 570: 'sold',\n",
       " 571: 'non',\n",
       " 572: 'along',\n",
       " 573: 'expect',\n",
       " 574: 'medium',\n",
       " 575: 'sour',\n",
       " 576: 'hooked',\n",
       " 577: 'given',\n",
       " 578: 'daily',\n",
       " 579: 'shipment',\n",
       " 580: 'espresso',\n",
       " 581: 'puppy',\n",
       " 582: 'hours',\n",
       " 583: 'certainly',\n",
       " 584: 'please',\n",
       " 585: 'difficult',\n",
       " 586: 'ground',\n",
       " 587: 'totally',\n",
       " 588: 'instant',\n",
       " 589: 'anywhere',\n",
       " 590: 'reasonable',\n",
       " 591: 'hit',\n",
       " 592: 'leave',\n",
       " 593: 'flour',\n",
       " 594: 'maker',\n",
       " 595: 'greenies',\n",
       " 596: 'rest',\n",
       " 597: 'locally',\n",
       " 598: 'within',\n",
       " 599: 'training',\n",
       " 600: 'due',\n",
       " 601: 'packets',\n",
       " 602: 'name',\n",
       " 603: 'stop',\n",
       " 604: 'sodium',\n",
       " 605: 'list',\n",
       " 606: 'adding',\n",
       " 607: 'lb',\n",
       " 608: 'sale',\n",
       " 609: 'licorice',\n",
       " 610: 'moist',\n",
       " 611: 'eats',\n",
       " 612: 'banana',\n",
       " 613: 'pork',\n",
       " 614: 'stopped',\n",
       " 615: 'cheap',\n",
       " 616: 'together',\n",
       " 617: 'anymore',\n",
       " 618: 'hair',\n",
       " 619: 'break',\n",
       " 620: 'easier',\n",
       " 621: 'based',\n",
       " 622: 'throw',\n",
       " 623: 'chews',\n",
       " 624: 'bowl',\n",
       " 625: 'worked',\n",
       " 626: 'shop',\n",
       " 627: 'pot',\n",
       " 628: 'fun',\n",
       " 629: 'pure',\n",
       " 630: 'thick',\n",
       " 631: 'satisfying',\n",
       " 632: 'done',\n",
       " 633: 'crazy',\n",
       " 634: 'stay',\n",
       " 635: 'vet',\n",
       " 636: 'yogurt',\n",
       " 637: 'leaves',\n",
       " 638: 'combination',\n",
       " 639: 'future',\n",
       " 640: 'finding',\n",
       " 641: 'true',\n",
       " 642: 'ingredient',\n",
       " 643: 'chili',\n",
       " 644: 'yum',\n",
       " 645: 'perfectly',\n",
       " 646: 'pet',\n",
       " 647: 'bland',\n",
       " 648: 'pricey',\n",
       " 649: 'substitute',\n",
       " 650: 'opinion',\n",
       " 651: 'area',\n",
       " 652: 'okay',\n",
       " 653: 'wow',\n",
       " 654: 'sell',\n",
       " 655: 'recipes',\n",
       " 656: 'almond',\n",
       " 657: 'raw',\n",
       " 658: 'become',\n",
       " 659: 'batch',\n",
       " 660: 'yes',\n",
       " 661: 'expecting',\n",
       " 662: 'completely',\n",
       " 663: 'summer',\n",
       " 664: 'entire',\n",
       " 665: 'packet',\n",
       " 666: 'five',\n",
       " 667: 'spices',\n",
       " 668: 'formula',\n",
       " 669: 'mint',\n",
       " 670: 'today',\n",
       " 671: 'maple',\n",
       " 672: 'decent',\n",
       " 673: 'option',\n",
       " 674: 'baked',\n",
       " 675: 'priced',\n",
       " 676: 'simple',\n",
       " 677: 'horrible',\n",
       " 678: 'pick',\n",
       " 679: 'finish',\n",
       " 680: 'seemed',\n",
       " 681: 'content',\n",
       " 682: 'none',\n",
       " 683: 'addition',\n",
       " 684: 'dish',\n",
       " 685: 'newman',\n",
       " 686: 'costco',\n",
       " 687: 'satisfied',\n",
       " 688: 'later',\n",
       " 689: 'twice',\n",
       " 690: 'kick',\n",
       " 691: 'mind',\n",
       " 692: 'discovered',\n",
       " 693: 'customer',\n",
       " 694: 'truly',\n",
       " 695: 'hoping',\n",
       " 696: 'favorites',\n",
       " 697: 'world',\n",
       " 698: 'carb',\n",
       " 699: 'normally',\n",
       " 700: 'count',\n",
       " 701: 'grain',\n",
       " 702: 'thin',\n",
       " 703: 'pancakes',\n",
       " 704: 'mixes',\n",
       " 705: 'tiny',\n",
       " 706: 'mess',\n",
       " 707: 'hint',\n",
       " 708: 'terrible',\n",
       " 709: 'bottom',\n",
       " 710: 'strawberry',\n",
       " 711: 'particular',\n",
       " 712: 'lower',\n",
       " 713: 'dont',\n",
       " 714: 'cooked',\n",
       " 715: 'noticed',\n",
       " 716: 'serve',\n",
       " 717: 'plan',\n",
       " 718: 'told',\n",
       " 719: 'mom',\n",
       " 720: 'hour',\n",
       " 721: 'unless',\n",
       " 722: 'door',\n",
       " 723: 'contains',\n",
       " 724: 'piece',\n",
       " 725: 'varieties',\n",
       " 726: 'awful',\n",
       " 727: 'prices',\n",
       " 728: 'body',\n",
       " 729: 'remember',\n",
       " 730: 'forward',\n",
       " 731: 'grey',\n",
       " 732: 'six',\n",
       " 733: 'vinegar',\n",
       " 734: 'blue',\n",
       " 735: 'issues',\n",
       " 736: 'bones',\n",
       " 737: 'label',\n",
       " 738: 'seen',\n",
       " 739: 'pie',\n",
       " 740: 'warm',\n",
       " 741: 'glass',\n",
       " 742: 'normal',\n",
       " 743: 'description',\n",
       " 744: 'roasted',\n",
       " 745: 'loose',\n",
       " 746: 'adds',\n",
       " 747: 'oh',\n",
       " 748: 'switch',\n",
       " 749: 'anyway',\n",
       " 750: 'everyday',\n",
       " 751: 'supermarket',\n",
       " 752: 'turned',\n",
       " 753: 'italian',\n",
       " 754: 'paid',\n",
       " 755: 'meals',\n",
       " 756: 'subscription',\n",
       " 757: 'return',\n",
       " 758: 'sweetener',\n",
       " 759: 'otherwise',\n",
       " 760: 'acid',\n",
       " 761: 'veggies',\n",
       " 762: 'stronger',\n",
       " 763: 'sticks',\n",
       " 764: 'bean',\n",
       " 765: 'drinker',\n",
       " 766: 'agree',\n",
       " 767: 'pleasant',\n",
       " 768: 'bring',\n",
       " 769: 'unlike',\n",
       " 770: 'felt',\n",
       " 771: 'ready',\n",
       " 772: 'mountain',\n",
       " 773: 'bears',\n",
       " 774: 'impressed',\n",
       " 775: 'liquid',\n",
       " 776: 'pods',\n",
       " 777: 'individual',\n",
       " 778: 'job',\n",
       " 779: 'taking',\n",
       " 780: 'touch',\n",
       " 781: 'salmon',\n",
       " 782: 'shape',\n",
       " 783: 'carrying',\n",
       " 784: 'earl',\n",
       " 785: 'candies',\n",
       " 786: 'ship',\n",
       " 787: 'uses',\n",
       " 788: 'contain',\n",
       " 789: 'feeling',\n",
       " 790: 'fit',\n",
       " 791: 'thinking',\n",
       " 792: 'matter',\n",
       " 793: 'homemade',\n",
       " 794: 'called',\n",
       " 795: 'office',\n",
       " 796: 'skin',\n",
       " 797: 'point',\n",
       " 798: 'changed',\n",
       " 799: 'crisp',\n",
       " 800: 'thrilled',\n",
       " 801: 'allergies',\n",
       " 802: 'party',\n",
       " 803: 'hands',\n",
       " 804: 'craving',\n",
       " 805: 'garlic',\n",
       " 806: 'caramel',\n",
       " 807: 'feeding',\n",
       " 808: 'results',\n",
       " 809: 'overpowering',\n",
       " 810: 'sealed',\n",
       " 811: 'somewhat',\n",
       " 812: 'chewing',\n",
       " 813: 'convenience',\n",
       " 814: 'immediately',\n",
       " 815: 'plenty',\n",
       " 816: 'set',\n",
       " 817: 'prepare',\n",
       " 818: 'ask',\n",
       " 819: 'overly',\n",
       " 820: 'nicely',\n",
       " 821: 'send',\n",
       " 822: 'flavoring',\n",
       " 823: 'chocolates',\n",
       " 824: 'berry',\n",
       " 825: 'shake',\n",
       " 826: 'carbs',\n",
       " 827: 'complaint',\n",
       " 828: 'wrapped',\n",
       " 829: 'breath',\n",
       " 830: 'raspberry',\n",
       " 831: 'eggs',\n",
       " 832: 'picture',\n",
       " 833: 'heavy',\n",
       " 834: 'pizza',\n",
       " 835: 'nearly',\n",
       " 836: 'dishes',\n",
       " 837: 'bigger',\n",
       " 838: 'lime',\n",
       " 839: 'afternoon',\n",
       " 840: 'gummy',\n",
       " 841: 'expiration',\n",
       " 842: 'style',\n",
       " 843: 'worst',\n",
       " 844: 'sea',\n",
       " 845: 'benefits',\n",
       " 846: 'mango',\n",
       " 847: 'fairly',\n",
       " 848: 'usual',\n",
       " 849: 'beverage',\n",
       " 850: 'crispy',\n",
       " 851: 'kitchen',\n",
       " 852: 'peanuts',\n",
       " 853: 'types',\n",
       " 854: 'share',\n",
       " 855: 'plant',\n",
       " 856: 'sick',\n",
       " 857: 'supply',\n",
       " 858: 'except',\n",
       " 859: 'person',\n",
       " 860: 'check',\n",
       " 861: 'source',\n",
       " 862: 'stuck',\n",
       " 863: 'mostly',\n",
       " 864: 'addicted',\n",
       " 865: 'served',\n",
       " 866: 'stevia',\n",
       " 867: 'call',\n",
       " 868: 'dented',\n",
       " 869: 'brought',\n",
       " 870: 'children',\n",
       " 871: 'near',\n",
       " 872: 'bbq',\n",
       " 873: 'lover',\n",
       " 874: 'nutrition',\n",
       " 875: 'gf',\n",
       " 876: 'supposed',\n",
       " 877: 'reading',\n",
       " 878: 'balance',\n",
       " 879: 'jelly',\n",
       " 880: 'pouch',\n",
       " 881: 'perhaps',\n",
       " 882: 'quantity',\n",
       " 883: 'mother',\n",
       " 884: 'walmart',\n",
       " 885: 'chance',\n",
       " 886: 'biscuits',\n",
       " 887: 'kinds',\n",
       " 888: 'trouble',\n",
       " 889: 'grams',\n",
       " 890: 'short',\n",
       " 891: 'weird',\n",
       " 892: 'ended',\n",
       " 893: 'running',\n",
       " 894: 'means',\n",
       " 895: 'jars',\n",
       " 896: 'replacement',\n",
       " 897: 'kept',\n",
       " 898: 'kid',\n",
       " 899: 'outside',\n",
       " 900: 'coming',\n",
       " 901: 'lbs',\n",
       " 902: 'alot',\n",
       " 903: 'pleasantly',\n",
       " 904: 'issue',\n",
       " 905: 'sorry',\n",
       " 906: 'fill',\n",
       " 907: 'standard',\n",
       " 908: 'brewed',\n",
       " 909: 'hate',\n",
       " 910: 'higher',\n",
       " 911: 'enjoys',\n",
       " 912: 'brewing',\n",
       " 913: 'dressing',\n",
       " 914: 'bake',\n",
       " 915: 'reviewers',\n",
       " 916: 'personally',\n",
       " 917: 'gummi',\n",
       " 918: 'heard',\n",
       " 919: 'gotten',\n",
       " 920: 'disappointing',\n",
       " 921: 'alone',\n",
       " 922: 'pops',\n",
       " 923: 'directions',\n",
       " 924: 'unique',\n",
       " 925: 'vegetables',\n",
       " 926: 'reviewer',\n",
       " 927: 'knew',\n",
       " 928: 'blends',\n",
       " 929: 'birthday',\n",
       " 930: 'blood',\n",
       " 931: 'pounds',\n",
       " 932: 'whatever',\n",
       " 933: 'grains',\n",
       " 934: 'gourmet',\n",
       " 935: 'portion',\n",
       " 936: 'working',\n",
       " 937: 'paying',\n",
       " 938: 'surprise',\n",
       " 939: 'number',\n",
       " 940: 'sort',\n",
       " 941: 'pour',\n",
       " 942: 'wine',\n",
       " 943: 'dessert',\n",
       " 944: 'sensitive',\n",
       " 945: 'cashews',\n",
       " 946: 'double',\n",
       " 947: 'china',\n",
       " 948: 'beautiful',\n",
       " 949: 'bitterness',\n",
       " 950: 'sample',\n",
       " 951: 'shelf',\n",
       " 952: 'tuna',\n",
       " 953: 'clear',\n",
       " 954: 'busy',\n",
       " 955: 'notice',\n",
       " 956: 'threw',\n",
       " 957: 'whenever',\n",
       " 958: 'straight',\n",
       " 959: 'cause',\n",
       " 960: 'mustard',\n",
       " 961: 'eater',\n",
       " 962: 'sauces',\n",
       " 963: 'fair',\n",
       " 964: 'receive',\n",
       " 965: 'helped',\n",
       " 966: 'brownies',\n",
       " 967: 'covered',\n",
       " 968: 'school',\n",
       " 969: 'careful',\n",
       " 970: 'tart',\n",
       " 971: 'potatoes',\n",
       " 972: 'fix',\n",
       " 973: 'sitting',\n",
       " 974: 'wants',\n",
       " 975: 'beer',\n",
       " 976: 'selling',\n",
       " 977: 'bits',\n",
       " 978: 'cracker',\n",
       " 979: 'note',\n",
       " 980: 'vegan',\n",
       " 981: 'creamer',\n",
       " 982: 'listed',\n",
       " 983: 'soups',\n",
       " 984: 'turn',\n",
       " 985: 'cool',\n",
       " 986: 'offered',\n",
       " 987: 'restaurant',\n",
       " 988: 'fridge',\n",
       " 989: 'mini',\n",
       " 990: 'cheddar',\n",
       " 991: 'tree',\n",
       " 992: 'pantry',\n",
       " 993: 'dairy',\n",
       " 994: 'switched',\n",
       " 995: 'tired',\n",
       " 996: 'lost',\n",
       " 997: 'instructions',\n",
       " 998: 'website',\n",
       " 999: 'boost',\n",
       " 1000: 'satisfy',\n",
       " ...}"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "src_tokenizer.index_word"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "amber-breeding",
   "metadata": {},
   "source": [
    "> inference 할 때는 인코더, 디코더 각각 모델을 만들어줘야 한다.  \n",
    "> 왜냐하면 훈련 때는 디코더에서 출력되는 것과 정답을 한꺼번에 확인할 수 있지만,  \n",
    "> inference 할 때는 디코더가 만들어질 문장만큼 반복 구조로 동작해야 하기 때문에(offset) 따로 만들어줘야 한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "armed-seller",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=3\n"
     ]
    }
   ],
   "source": [
    "# 인코더 설계\n",
    "encoder_model = Model(inputs=encoder_inputs, outputs=[encoder_outputs, state_h, state_c])\n",
    "\n",
    "# 이전 시점의 상태들을 저장하는 텐서\n",
    "decoder_state_input_h = Input(shape=(hidden_size,))\n",
    "decoder_state_input_c = Input(shape=(hidden_size,))\n",
    "\n",
    "dec_emb2 = dec_emb_layer(decoder_inputs)\n",
    "\n",
    "# 문장의 다음 단어를 예측하기 위해서 초기 상태(initial_state)를 이전 시점의 상태로 사용. 이는 뒤의 함수 decode_sequence()에 구현\n",
    "# 훈련 과정에서와 달리 LSTM의 리턴하는 은닉 상태와 셀 상태인 state_h와 state_c를 버리지 않음.\n",
    "decoder_outputs2, state_h2, state_c2 = decoder_lstm(dec_emb2, initial_state=[decoder_state_input_h, decoder_state_input_c])\n",
    "\n",
    "print('=3')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "standard-algorithm",
   "metadata": {},
   "source": [
    "> 어텐션 메커니즘 사용하는 출력층 설계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "judicial-baseline",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=3\n"
     ]
    }
   ],
   "source": [
    "# 어텐션 함수\n",
    "decoder_hidden_state_input = Input(shape=(text_max_len, hidden_size))\n",
    "attn_out_inf = attn_layer([decoder_outputs2, decoder_hidden_state_input])\n",
    "decoder_inf_concat = Concatenate(axis=-1, name='concat')([decoder_outputs2, attn_out_inf])\n",
    "\n",
    "# 디코더의 출력층\n",
    "decoder_outputs2 = decoder_softmax_layer(decoder_inf_concat) \n",
    "\n",
    "# 최종 디코더 모델\n",
    "decoder_model = Model(\n",
    "    [decoder_inputs] + [decoder_hidden_state_input,decoder_state_input_h, decoder_state_input_c],\n",
    "    [decoder_outputs2] + [state_h2, state_c2])\n",
    "\n",
    "print('=3')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "starting-academy",
   "metadata": {},
   "source": [
    "> 단어 완성하는 함수 정의"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "bearing-drilling",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=3\n"
     ]
    }
   ],
   "source": [
    "def decode_sequence(input_seq):\n",
    "    # 입력으로부터 인코더의 상태를 얻음\n",
    "    e_out, e_h, e_c = encoder_model.predict(input_seq)\n",
    "\n",
    "     # <SOS>에 해당하는 토큰 생성\n",
    "    target_seq = np.zeros((1,1))\n",
    "    target_seq[0, 0] = tar_word_to_index['sostoken']\n",
    "\n",
    "    stop_condition = False\n",
    "    decoded_sentence = ''\n",
    "    while not stop_condition: # stop_condition이 True가 될 때까지 루프 반복\n",
    "\n",
    "        output_tokens, h, c = decoder_model.predict([target_seq] + [e_out, e_h, e_c])\n",
    "        sampled_token_index = np.argmax(output_tokens[0, -1, :])\n",
    "        sampled_token = tar_index_to_word[sampled_token_index]\n",
    "\n",
    "        if (sampled_token!='eostoken'):\n",
    "            decoded_sentence += ' '+sampled_token\n",
    "\n",
    "        #  <eos>에 도달하거나 최대 길이를 넘으면 중단.\n",
    "        if (sampled_token == 'eostoken'  or len(decoded_sentence.split()) >= (summary_max_len-1)):\n",
    "            stop_condition = True\n",
    "\n",
    "        # 길이가 1인 타겟 시퀀스를 업데이트\n",
    "        target_seq = np.zeros((1,1))\n",
    "        target_seq[0, 0] = sampled_token_index\n",
    "\n",
    "        # 상태를 업데이트 합니다.\n",
    "        e_h, e_c = h, c\n",
    "\n",
    "    return decoded_sentence\n",
    "print('=3')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "individual-range",
   "metadata": {},
   "source": [
    "## 모델 테스트하기"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "correct-analyst",
   "metadata": {},
   "source": [
    "> - 해석하기 쉽게 정수->텍스트로 만들어주는 함수 만들기  \n",
    "> - Text에서는 패딩인 0만 제외하고  \n",
    "> - Summary에서는 시작토큰과 종료토큰도 제외"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "critical-elimination",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=3\n"
     ]
    }
   ],
   "source": [
    "# 원문의 정수 시퀀스를 텍스트 시퀀스로 변환\n",
    "def seq2text(input_seq):\n",
    "    temp=''\n",
    "    for i in input_seq:\n",
    "        if (i!=0):\n",
    "            temp = temp + src_index_to_word[i]+' '\n",
    "    return temp\n",
    "\n",
    "# 요약문의 정수 시퀀스를 텍스트 시퀀스로 변환\n",
    "def seq2summary(input_seq):\n",
    "    temp=''\n",
    "    for i in input_seq:\n",
    "        if ((i!=0 and i!=tar_word_to_index['sostoken']) and i!=tar_word_to_index['eostoken']):\n",
    "            temp = temp + tar_index_to_word[i] + ' '\n",
    "    return temp\n",
    "\n",
    "print('=3')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "agricultural-north",
   "metadata": {},
   "source": [
    "> 테스트로 50개만 해보기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "still-briefing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "원문 : friend mine sweet enough buy discovered tiny market still grateful willing buy without knowing good even favourite spaghetti sauce could hide pasty wet dog pasta gives flavour forget directions followed stuff worth one table liked stuff another disappointing gluten free pasta waste money \n",
      "실제 요약 : awful \n",
      "예측 요약 :  not bad\n",
      "\n",
      "\n",
      "원문 : found better english style tea usa brew one cup time usually add splash milk table sugar flavorful comforting \n",
      "실제 요약 : delicious english style tea \n",
      "예측 요약 :  great tea\n",
      "\n",
      "\n",
      "원문 : love product organic use making whole grain breads grind flour electric stone flour mill years like real quality ingredients homemade products organic valley milk one ingredients \n",
      "실제 요약 : good quality at great value \n",
      "예측 요약 :  great product\n",
      "\n",
      "\n",
      "원문 : always love salmon however difficult eat go packages filled void perfection drawback small portion size price otherwise product exceptional salmon overly dried still contains small amount moisture allow pleasant eating experience order bags monthly refill program offset cost degree feel product worth money try \n",
      "실제 요약 : superb \n",
      "예측 요약 :  great gummy bears\n",
      "\n",
      "\n",
      "원문 : try maintain low carb diet time excited find tried thought absolutely horrible could get past smell really bad could get past fact felt like eating unflavored gummy worms almost gagged mixed alfredo sauce broccoli chicken maybe would ok something bold flavor unfortunately willing try find \n",
      "실제 요약 : not what was expecting \n",
      "예측 요약 :  horrible\n",
      "\n",
      "\n",
      "원문 : candies addicting available certain neighborhood tub perfect disappointed like coconut caramel \n",
      "실제 요약 : delicious \n",
      "예측 요약 :  delicious\n",
      "\n",
      "\n",
      "원문 : found davidson assam tea good assam reasonable price reason give five stars better twice price davidson hands winner value second time purchased intend buy future \n",
      "실제 요약 : davidson bulk tea \n",
      "예측 요약 :  great product\n",
      "\n",
      "\n",
      "원문 : words come mind amazing wonderful delicious fantastic delightful thank snyder pieces bravo next best thing actual buffalo chicken wing \n",
      "실제 요약 : get some \n",
      "예측 요약 :  delicious\n",
      "\n",
      "\n",
      "원문 : puppy delicate digestive tract constantly cleaning diarrhea got wellness lamb sweet potato since sick year old full pep energy healthy looking coat sickness truly star food dog \n",
      "실제 요약 : at last my dog is well with wellness \n",
      "예측 요약 :  great food\n",
      "\n",
      "\n",
      "원문 : first time salty also heavy ham taste love ham much \n",
      "실제 요약 : too salty and ham taste \n",
      "예측 요약 :  good stuff\n",
      "\n",
      "\n",
      "원문 : rose powder potent good making rose water well packaged \n",
      "실제 요약 : smells like \n",
      "예측 요약 :  hour energy\n",
      "\n",
      "\n",
      "원문 : received order quickly actually used yet unless mix ends bugs assuming fine cannot find product grocery stores anymore like use combined jiffy cornbread mix great tasting cornbread \n",
      "실제 요약 : jiffy cake mix \n",
      "예측 요약 :  great product\n",
      "\n",
      "\n",
      "원문 : used buy product local walmart longer carry delivers garlic flavor per ounce ordinary garlic powder use whenever fresh garlic practical burgers potato salad etc \n",
      "실제 요약 : much better than garlic powder \n",
      "예측 요약 :  great flavor\n",
      "\n",
      "\n",
      "원문 : love drink taste soda sweet carbonated ingredients carbonated water juice natural flavors vitamin ounce serving size perfect go meal thirst quencher shape long tall easy hold parents whose kids soda might find product good substitute also great adults crave carbonation preservatives \n",
      "실제 요약 : delicious carbonated beverage without ingredients \n",
      "예측 요약 :  great alternative to soda\n",
      "\n",
      "\n",
      "원문 : bought years always enjoys much knows word dingo means dingo treat time \n",
      "실제 요약 : old by that still loves \n",
      "예측 요약 :  great product\n",
      "\n",
      "\n",
      "원문 : packaging excellent quality fudge expectations especially reading reviews love mint really excited fudge felt fudge sweet mint flavor fudge overpowered sweetness probably buy \n",
      "실제 요약 : just ok \n",
      "예측 요약 :  excellent\n",
      "\n",
      "\n",
      "원문 : pick grocery store every sister laws think compare home made course much better like tortillas pita bread taste like cardboard nice potato cream flavor great sandwiches peanut butter jelly traditional butter sugar usually cheese fruit \n",
      "실제 요약 : its not bad \n",
      "예측 요약 :  great for gluten free\n",
      "\n",
      "\n",
      "원문 : organic tummy best type ever side effects regular relief within minutes bloating gas upset tummy full feeling eating much something rich better results st products products \n",
      "실제 요약 : safe and soothing \n",
      "예측 요약 :  great for\n",
      "\n",
      "\n",
      "원문 : vanilla caramel eh caramel boxes french vanilla came bag plastic bag busted leaking probably shipping understand caramel ones come boxes french vanilla comes bag like flavor like way shipped \n",
      "실제 요약 : why not boxes \n",
      "예측 요약 :  not bad\n",
      "\n",
      "\n",
      "원문 : lot great coffees around simply best smooth buttery subtle nutty undertones bitterness medium roast full flavor perfect everyday cup beans aromatic make mouth water anticipation great cup coffee find coffee restaurants flavor bit different restaurant version sweet taste beans sweetness give much fuller flavor similar flavor would expect gourmet blend \n",
      "실제 요약 : really great coffee \n",
      "예측 요약 :  smooth coffee\n",
      "\n",
      "\n",
      "원문 : buster shelter dog little picky eating sometimes tried lot food treats things eat allergic zuke line products favorites tried several items zuke line loves jerky naturals lamb strong smell really goes usa made highly recommended \n",
      "실제 요약 : loves these \n",
      "예측 요약 :  great for my dog\n",
      "\n",
      "\n",
      "원문 : wanted try coffee life happy company mailed timely manner sent \n",
      "실제 요약 : coffee \n",
      "예측 요약 :  great coffee\n",
      "\n",
      "\n",
      "원문 : honestly say bars better thought would get chocolate little bits peanut butter granola actually went bought great go keep one purse case keep extra work overall bad snack meal replacement although done sometimes situation cant helped im huge fan chocolate fact wasnt overpowering bonus \n",
      "실제 요약 : not bad \n",
      "예측 요약 :  great taste\n",
      "\n",
      "\n",
      "원문 : enjoyed packaged oatmeal thought trying noodle soup sadly probably worst purchase made amazon soup taste noodles taste like paper seasoning good wish could return remaining cups think amazon take back food items waste \n",
      "실제 요약 : has no taste very dissapointed in this purchase \n",
      "예측 요약 :  not bad\n",
      "\n",
      "\n",
      "원문 : thought would worth trying due price cats really disliked bowl food older cat happily eat almost anything one two would walk away \n",
      "실제 요약 : both of my cats hated it \n",
      "예측 요약 :  great product\n",
      "\n",
      "\n",
      "원문 : jaw remember childhood getting increasingly difficult find jaw old fashioned kind big last long time sweet usual selection flavors orange cherry grape lemon lime like except lemon bitter cherry orange favorites find eat one night satisfied versus chewy candy seems lead naturally one half bag \n",
      "실제 요약 : excellent \n",
      "예측 요약 :  good stuff\n",
      "\n",
      "\n",
      "원문 : best coffee ever cannot satisfied without wonderful product \n",
      "실제 요약 : the best \n",
      "예측 요약 :  great coffee\n",
      "\n",
      "\n",
      "원문 : super disappointed orbit discontinued favorite flavor gum many flavors like came sangria thrilled appeared gone amazon true word let know gum still ordered two cartons order based continued availability thanks amazon \n",
      "실제 요약 : they found my favorite gum \n",
      "예측 요약 :  great gum\n",
      "\n",
      "\n",
      "원문 : bought small pack quest see dog would chew dog breath product luck sure dog cough expensive shipping small pack like ordering dollars worth \n",
      "실제 요약 : dog will not bite \n",
      "예측 요약 :  good product\n",
      "\n",
      "\n",
      "원문 : love keurig happy find less expensive product put liked concept plastic free packaging rainforest blend tastes like smells like wet disappointed \n",
      "실제 요약 : rainforest \n",
      "예측 요약 :  great coffee\n",
      "\n",
      "\n",
      "원문 : love product taste good better real movie theatre pop corn fast easy prepare \n",
      "실제 요약 : delicious \n",
      "예측 요약 :  great product\n",
      "\n",
      "\n",
      "원문 : favorite matcha tried sweet comforting rich love made healthy tea mm perfect choice cold afternoon good matcha flavor try never tried matcha \n",
      "실제 요약 : yum \n",
      "예측 요약 :  great tea\n",
      "\n",
      "\n",
      "원문 : actually little surprised good tried lower calorie peanut butters like better butter actually spit tasted artificial go bell plantation site company makes stuff lots recipes look appetizing problem powdered peanut butter satisfying real stuff fat sometimes hard eat little like would normal peanut butter taste natural fake tasting way loved \n",
      "실제 요약 : it is surprisingly good \n",
      "예측 요약 :  great product\n",
      "\n",
      "\n",
      "원문 : great product healthy dogs first food love eat helped older dog lose weight year old lab gain weight needed healthy \n",
      "실제 요약 : canidae dry dog food \n",
      "예측 요약 :  great for dogs\n",
      "\n",
      "\n",
      "원문 : college looking healthy snack take class sure product first tried love admit taste buds adjust natural flavors especially use bars lot artificial ingredients local kroger tried almost flavors apple pie cashew cookie lemon favorites \n",
      "실제 요약 : love larabars \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "예측 요약 :  great tasting snack\n",
      "\n",
      "\n",
      "원문 : contain organic ingredients doggies gobbled say dog treat one dogs little fussy sometimes turns nose treats problem asking math decide organic dog treats worth extra cash giving dogs healthiest treats priority cannot see going wrong cannot comment flavor \n",
      "실제 요약 : the doggies loved them \n",
      "예측 요약 :  my dogs loved them\n",
      "\n",
      "\n",
      "원문 : everyone loves chocolate wafer used make cake look like box impressive company excellent customer service helpful sending replacement shipment broken purchasing future \n",
      "실제 요약 : great gift for dad \n",
      "예측 요약 :  great product\n",
      "\n",
      "\n",
      "원문 : first purchase water tastes great bottles thicker also everyone likes name haha \n",
      "실제 요약 : great tasting water \n",
      "예측 요약 :  great product\n",
      "\n",
      "\n",
      "원문 : began using product four weeks ago absolutely love thrown given away products used hair stronger soft color relax hair since using breakage healing hair increasingly less use damage color along ultra shea products sold product try regret really says \n",
      "실제 요약 : wonderful \n",
      "예측 요약 :  great product\n",
      "\n",
      "\n",
      "원문 : love cookies love erin result cookies make smile texture makes eyes smell reminds beautiful spring days awesome would spend erin bring fresh cookies oven us enjoying together outdoor swing listening birds enjoying creek passed backyard thing kind kind differ much taste taste taste heaven \n",
      "실제 요약 : you \n",
      "예측 요약 :  great cookies\n",
      "\n",
      "\n",
      "원문 : wife bought system would access starbucks agree better love \n",
      "실제 요약 : excellent \n",
      "예측 요약 :  great coffee\n",
      "\n",
      "\n",
      "원문 : alot bbq sauce price hawaii live georgia due military never cheap order family send home product came great price like classic natural bbq taste try especially use chicken marinate hrs add sugar marinade want sweet side meat hits grill know made right choice smell \n",
      "실제 요약 : bang for your buck \n",
      "예측 요약 :  great product\n",
      "\n",
      "\n",
      "원문 : use many pastas definitely top line little buying products \n",
      "실제 요약 : the best \n",
      "예측 요약 :  best mustard\n",
      "\n",
      "\n",
      "원문 : daughters doctor gave pellets order comes blue container take pellets tongue times day days increase supply quickly noticed day increase better fenugreek cause side effects baby either \n",
      "실제 요약 : says it works but \n",
      "예측 요약 :  good for\n",
      "\n",
      "\n",
      "원문 : consider shortbread cut mustard nice packaging bitter tart taste expected \n",
      "실제 요약 : not so good \n",
      "예측 요약 :  great taste\n",
      "\n",
      "\n",
      "원문 : looking vegetarian rawhide dogs best yet sure last hours gives good minutes chewing dogs love get excited site bag wish local stores carried order time \n",
      "실제 요약 : my dogs love em \n",
      "예측 요약 :  great product\n",
      "\n",
      "\n",
      "원문 : ordered bags contained cinnamon stick amazon yummy earth surely made mistake amazon reasonable money \n",
      "실제 요약 : wrong item in package \n",
      "예측 요약 :  not what expected\n",
      "\n",
      "\n",
      "원문 : really like cocoa butter makes excellent chocolates would definately order good quality shipped quickly happy overall \n",
      "실제 요약 : awesome stuff \n",
      "예측 요약 :  great product\n",
      "\n",
      "\n",
      "원문 : ring pops came container looks like pictured individually wrapped shown label different fine disclaimer right product description ring pops came good condition pieces enjoyable ring pop may flavors got cherry watermelon blue raspberry strawberry berry blast twisted blue raspberry watermelon may vary \n",
      "실제 요약 : ring pops \n",
      "예측 요약 :  not what expected\n",
      "\n",
      "\n",
      "원문 : ring taste like peach ring rather probably worst jelly entire life though hates sweetness might find jelly tasty anyone tries buy yucky stuff \n",
      "실제 요약 : not peach ring but rather rubber jelly \n",
      "예측 요약 :  not bad\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(50, 100):\n",
    "    print(\"원문 :\", seq2text(encoder_input_test[i]))\n",
    "    print(\"실제 요약 :\", seq2summary(decoder_input_test[i]))\n",
    "    print(\"예측 요약 :\", decode_sequence(encoder_input_test[i].reshape(1, text_max_len)))\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "loved-processor",
   "metadata": {},
   "source": [
    "## 결론\n",
    "- 좋은 요약도 있고, 안 좋은 요약도 있는데 seq2seq와 어텐션의 자체의 조합을 좀 더 좋게 수정하는 방법도 있고, 빔 서치(beam search), 사전 훈련된 워드 임베딩(pre-trained word embedding), 또는 인코더 <-> 디코더 자체의 구조를 새로이 변경한 하는 트랜스포머(Transformer)를 적용하면 성능이 개선된다.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aquatic-perth",
   "metadata": {},
   "source": [
    "---------------------------------------------------------\n",
    "---------------------------------------------------------\n",
    "---------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caring-coach",
   "metadata": {},
   "source": [
    "## 추출적 요약 해보기\n",
    "\n",
    "- 위에는 추상적 요약을 진행했음\n",
    "- `summa` 패키지를 통해 간단히 해볼 수 있음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "internal-apache",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "summa                         1.2.0\n",
      "\u001b[33mWARNING: You are using pip version 20.3.3; however, version 21.3.1 is available.\n",
      "You should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip list | grep summa"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suited-programming",
   "metadata": {},
   "source": [
    "### 데이터 다운로드하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "retained-alliance",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from summa.summarizer import summarize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "everyday-certificate",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = requests.get('http://rare-technologies.com/the_matrix_synopsis.txt').text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "recreational-authorization",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The screen is filled with green, cascading code which gives way to the title, The Matrix.\r\n",
      "\r\n",
      "A phone rings and text appears on the screen: \"Call trans opt: received. 2-19-98 13:24:18 REC: Log>\" As a conversation takes place between Trinity (Carrie-Anne Moss) and Cypher (Joe Pantoliano), two free humans, a table of random green numbers are being scanned and individual numbers selected, creating a series of digits not unlike an ordinary phone number, as if a code is being deciphered or a call is being traced.\r\n",
      "\r\n",
      "Trinity discusses some unknown person. Cypher taunts Trinity, suggesting she enjoys watching him. Trinity counters that \"Morpheus (Laurence Fishburne) says he may be 'the One',\" just as the sound of a number being selected alerts Trinity that someone may be tracing their call. She ends the call.\r\n",
      "\r\n",
      "Armed policemen move down a darkened, decrepit hallway in the Heart O' the City Hotel, their flashlight beam bouncing just ahead of them. They come to room 303, kick down the door and find a woman dressed in black, facing away from them. It's Trinity. She brings her hands up from the laptop she's working on at their command.\r\n",
      "\r\n",
      "Outside the hotel a car drives up and three agents appear in neatly pressed black suits. They are Agent Smith (Hugo Weaving), Agent Brown (Paul Goddard), and Agent Jones (Robert Taylor). Agent Smith and the presiding police lieutenant argue. Agent Smith admonishes the policeman that they were given specific orders to contact the agents first, for their\n"
     ]
    }
   ],
   "source": [
    "# 매트릭스의 시놉시스임\n",
    "print(text[:1500])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "developmental-james",
   "metadata": {},
   "source": [
    "## summarize 사용하기\n",
    "\n",
    "> 파라미터 설명\n",
    "> - text (str) : 요약할 테스트.\n",
    "> - ratio (float, optional) – 요약문에서 원본에서 선택되는 문장 비율. 0~1 사이값\n",
    "> - words (int or None, optional) – 출력에 포함할 단어 수. 만약, ratio와 함께 두 파라미터가 모두 제공되는 경우 ratio는 무시한다.\n",
    "> - split (bool, optional) – True면 문장 list / False는 조인(join)된 문자열을 반환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "reserved-finger",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary:\n",
      "Morpheus, Trinity, Neo, Apoc, Switch, Mouse and Cypher are jacked into the Matrix.\n",
      "Trinity brings the helicopter down to the floor that Morpheus is on and Neo opens fire on the three Agents.\n"
     ]
    }
   ],
   "source": [
    "print('Summary:')\n",
    "print(summarize(text, ratio=0.005))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "southwest-trunk",
   "metadata": {},
   "source": [
    "> `summa`의 `summarize()`는 별도로 토큰화 하지 않아도 됨. 내부적으로 알아서 함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "opposed-status",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary:\n",
      "['Morpheus, Trinity, Neo, Apoc, Switch, Mouse and Cypher are jacked into the Matrix.', 'Trinity brings the helicopter down to the floor that Morpheus is on and Neo opens fire on the three Agents.']\n"
     ]
    }
   ],
   "source": [
    "# join해서 문장(str)로 반환하지말고 list로 반환하기\n",
    "print('Summary:')\n",
    "print(summarize(text, ratio=0.005, split=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "destroyed-worcester",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary:\n",
      "Trinity takes Neo to Morpheus.\n",
      "Morpheus, Trinity, Neo, Apoc, Switch, Mouse and Cypher are jacked into the Matrix.\n",
      "Trinity brings the helicopter down to the floor that Morpheus is on and Neo opens fire on the three Agents.\n"
     ]
    }
   ],
   "source": [
    "# 단어 수 선택\n",
    "print('Summary:')\n",
    "print(summarize(text, words=50))"
   ]
  }
 ],
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