{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5699\n",
      "9823\n",
      "14020\n",
      "2727\n",
      "1498\n",
      "1464\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "import re\n",
    "\n",
    "\"\"\"\n",
    "thriller_plot = pd.read_csv('/Users/yangyoonji/Documents/2020-1/2020-dataCapstone/data/moviedata/moviePlot/thrillerPlot.csv')\n",
    "drama_plot = pd.read_csv('/Users/yangyoonji/Documents/2020-1/2020-dataCapstone/data/moviedata/moviePlot/dramaPlot.csv')\n",
    "fantasy_plot = pd.read_csv('/Users/yangyoonji/Documents/2020-1/2020-dataCapstone/data/moviedata/moviePlot/fantasyPlot.csv')\n",
    "history_plot = pd.read_csv('/Users/yangyoonji/Documents/2020-1/2020-dataCapstone/data/moviedata/moviePlot/historyPlot.csv')\n",
    "social_plot = pd.read_csv('/Users/yangyoonji/Documents/2020-1/2020-dataCapstone/data/moviedata/moviePlot/socialPlot.csv')\n",
    "romance_plot = pd.read_csv('/Users/yangyoonji/Documents/2020-1/2020-dataCapstone/data/moviedata/moviePlot/romancePlot.csv')\n",
    "musical_plot = pd.read_csv('/Users/yangyoonji/Documents/2020-1/2020-dataCapstone/data/musicalData/broadMusicalPlot.csv',encoding='cp949')\n",
    "\n",
    "# /Users/김서영/Desktop/datacap/data/moviedata/moviePlot/romancePlot.csv\n",
    "\"\"\"\n",
    "romance_plot = pd.read_csv('/Users/김서영/Desktop/datacap/data/moviedata/moviePlot/romancePlot.csv')\n",
    "thriller_plot = pd.read_csv('/Users/김서영/Desktop/datacap/data/moviedata/moviePlot/thrillerPlot.csv')\n",
    "drama_plot = pd.read_csv('/Users/김서영/Desktop/datacap/data/moviedata/moviePlot/dramaPlot.csv')\n",
    "fantasy_plot = pd.read_csv('/Users/김서영/Desktop/datacap/data/moviedata/moviePlot/fantasyPlot.csv')\n",
    "history_plot = pd.read_csv('/Users/김서영/Desktop/datacap/data/moviedata/moviePlot/historyPlot.csv')\n",
    "social_plot = pd.read_csv('/Users/김서영/Desktop/datacap/data/moviedata/moviePlot/socialPlot.csv')\n",
    "\n",
    "musical_plot = pd.read_csv('/Users/김서영/Desktop/datacap/data/musicalData/broadMusicalPlot.csv',encoding='cp949')\n",
    "\n",
    "\n",
    "print(len(romance_plot)) #5699 ==> train 2500 test 2500\n",
    "print(len(thriller_plot)) #9823 ==> train 2500 test 2500\n",
    "print(len(drama_plot))\n",
    "print(len(fantasy_plot))\n",
    "print(len(history_plot))\n",
    "print(len(social_plot))\n",
    "\n",
    "train_data_size = 1463\n",
    "test_data_size = 1463\n",
    "\n",
    "#전처리(1) 전부 소문자로 변환\n",
    "\n",
    "\n",
    "#romance_plot.줄거리 = romance_plot.줄거리.str.lower()\n",
    "#thriller_plot.줄거리 = thriller_plot.줄거리.str.lower()\n",
    "\n",
    "#전처리(1-1) 데이터 csv 파일로 옮기기\n",
    "#romance_plot 2899개 train_data로 to_csv || 2800개 test_data로 to_csv\n",
    "#thriller_plot 2899개 train_data로 to_csv || 2800개 test_data로 to_csv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "RM = [[] for _ in range(5699)]\n",
    "for i in range(5699):\n",
    "    RM[i].append(''.join(romance_plot.줄거리[i]))\n",
    "    \n",
    "TH = [[] for _ in range(9823)]\n",
    "for i in range(9823):\n",
    "    TH[i].append(''.join(thriller_plot.줄거리[i]))\n",
    "\n",
    "FN = [[] for _ in range(2727)]\n",
    "for i in range(2727):\n",
    "    FN[i].append(''.join(fantasy_plot.줄거리[i]))\n",
    "    \n",
    "HS = [[] for _ in range(1498)]\n",
    "for i in range(1498):\n",
    "    HS[i].append(''.join(history_plot.줄거리[i]))\n",
    "    \n",
    "SC = [[] for _ in range(1464)]\n",
    "for i in range(1464):\n",
    "    SC[i].append(''.join(social_plot.줄거리[i]))\n",
    "\n",
    "DR = [[] for _ in range(14019)]\n",
    "for i in range(14019):\n",
    "    DR[i].append(''.join(drama_plot.줄거리[i]))\n",
    "    \n",
    "Mu = [[] for _ in range(307)]\n",
    "for i in range(307):\n",
    "    Mu[i].append(''.join(musical_plot.muplot[i]))\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "allplot = RM+TH+FN+HS+SC+DR #모든 드라마 줄거리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35230\n",
      "307\n"
     ]
    }
   ],
   "source": [
    "print(len(allplot))\n",
    "print(len(musical_plot))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 모든 장르 줄거리 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████| 35230/35230 [02:26<00:00, 240.55it/s]\n"
     ]
    }
   ],
   "source": [
    "# 토큰화+전처리(3) 전체 불용어 처리\n",
    "# 전체 플롯\n",
    "from tqdm import tqdm\n",
    "all_vocab = {} \n",
    "all_sentences = []\n",
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "for i in tqdm(allplot):\n",
    "    all_sentences = word_tokenize(str(i)) # 단어 토큰화를 수행합니다.\n",
    "    result = []\n",
    "    for word in all_sentences: \n",
    "        word = word.lower() # 모든 단어를 소문자화하여 단어의 개수를 줄입니다.\n",
    "        if word not in stop_words: # 단어 토큰화 된 결과에 대해서 불용어를 제거합니다.\n",
    "            if len(word) > 2: # 단어 길이가 2이하인 경우에 대하여 추가로 단어를 제거합니다.\n",
    "                result.append(word)\n",
    "                if word not in all_vocab:\n",
    "                    all_vocab[word] = 0 \n",
    "                all_vocab[word] += 1\n",
    "    all_sentences.append(result) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_vocab_sorted = sorted(all_vocab.items(), key = lambda x:x[1], reverse = True)\n",
    "\n",
    "#전처리(4) 인덱스 부여\n",
    "all_word_to_index = {}\n",
    "i=0\n",
    "for (word, frequency) in all_vocab_sorted :\n",
    "    if frequency > 1 : # 정제(Cleaning) 챕터에서 언급했듯이 빈도수가 적은 단어는 제외한다.\n",
    "        i=i+1\n",
    "        all_word_to_index[word] = i\n",
    "#print(all_word_to_index)\n",
    "\n",
    "vocab_size = 15000 #상위 15000개 단어만 사용\n",
    "words_frequency = [w for w,c in all_word_to_index.items() if c >= vocab_size + 1] # 인덱스가 200 초과인 단어 제거\n",
    "for w in words_frequency:\n",
    "    del all_word_to_index[w] # 해당 단어에 대한 인덱스 정보를 삭제\n",
    "\n",
    "    \n",
    "all_word_to_index['OOV'] = len(all_word_to_index) + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 로맨스"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 5699/5699 [00:21<00:00, 267.88it/s]\n"
     ]
    }
   ],
   "source": [
    "# 토큰화+전처리(3) 불용어 처리\n",
    "# 로맨스 플롯\n",
    "\n",
    "vocab_r = {} \n",
    "RMsentences = []\n",
    "RMstop_words = set(stopwords.words('english'))\n",
    "\n",
    "for i in tqdm(RM):\n",
    "    RMsentence = word_tokenize(str(i)) # 단어 토큰화를 수행합니다.\n",
    "    result = []\n",
    "\n",
    "    for word in RMsentence: \n",
    "        word = word.lower() # 모든 단어를 소문자화하여 단어의 개수를 줄입니다.\n",
    "        if word not in RMstop_words: # 단어 토큰화 된 결과에 대해서 불용어를 제거합니다.\n",
    "            if len(word) > 2: # 단어 길이가 2이하인 경우에 대하여 추가로 단어를 제거합니다.\n",
    "                result.append(word)\n",
    "                if word not in vocab_r:\n",
    "                    vocab_r[word] = 0 \n",
    "                vocab_r[word] += 1\n",
    "    RMsentences.append(result) \n",
    "\n",
    "R_encoded = []\n",
    "for s in RMsentences:\n",
    "    temp = []\n",
    "    for w in s:\n",
    "        try:\n",
    "            temp.append(all_word_to_index[w])\n",
    "        except KeyError:\n",
    "            temp.append(all_word_to_index['OOV'])\n",
    "    R_encoded.append(temp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 스릴러"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 9823/9823 [00:40<00:00, 240.93it/s]\n"
     ]
    }
   ],
   "source": [
    "# 토큰화+전처리(3) 불용어 처리\n",
    "# 스릴러 플롯\n",
    "\n",
    "vocab_th = {} \n",
    "THsentences = []\n",
    "THstop_words = set(stopwords.words('english'))\n",
    "\n",
    "for i in tqdm(TH):\n",
    " \n",
    "    THsentence = word_tokenize(str(i)) # 단어 토큰화를 수행합니다.\n",
    "    result = []\n",
    "\n",
    "    for word in THsentence: \n",
    "        word = word.lower() # 모든 단어를 소문자화하여 단어의 개수를 줄입니다.\n",
    "        if word not in THstop_words: # 단어 토큰화 된 결과에 대해서 불용어를 제거합니다.\n",
    "            if len(word) > 2: # 단어 길이가 2이하인 경우에 대하여 추가로 단어를 제거합니다.\n",
    "                result.append(word)\n",
    "                if word not in vocab_th:\n",
    "                    vocab_th[word] = 0 \n",
    "                vocab_th[word] += 1\n",
    "    THsentences.append(result) \n",
    "\n",
    "TH_encoded = []\n",
    "for s in THsentences:\n",
    "    temp = []\n",
    "    for w in s:\n",
    "        try:\n",
    "            temp.append(all_word_to_index[w])\n",
    "        except KeyError:\n",
    "            temp.append(all_word_to_index['OOV'])\n",
    "    TH_encoded.append(temp)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 역사"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 1498/1498 [00:05<00:00, 267.17it/s]\n"
     ]
    }
   ],
   "source": [
    "vocab_HS = {} \n",
    "HSsentences = []\n",
    "HSstop_words = set(stopwords.words('english'))\n",
    "\n",
    "for i in tqdm(HS):\n",
    "    \n",
    "    HSsentence = word_tokenize(str(i)) # 단어 토큰화를 수행합니다.\n",
    "    result = []\n",
    "\n",
    "    for word in HSsentence: \n",
    "        word = word.lower() # 모든 단어를 소문자화하여 단어의 개수를 줄입니다.\n",
    "        if word not in HSstop_words: # 단어 토큰화 된 결과에 대해서 불용어를 제거합니다.\n",
    "            if len(word) > 2: # 단어 길이가 2이하인 경우에 대하여 추가로 단어를 제거합니다.\n",
    "                result.append(word)\n",
    "                if word not in vocab_HS:\n",
    "                    vocab_HS[word] = 0 \n",
    "                vocab_HS[word] += 1\n",
    "    HSsentences.append(result) \n",
    "\n",
    "HS_encoded = []\n",
    "for s in HSsentences:\n",
    "    temp = []\n",
    "    for w in s:\n",
    "        try:\n",
    "            temp.append(all_word_to_index[w])\n",
    "        except KeyError:\n",
    "            temp.append(all_word_to_index['OOV'])\n",
    "    HS_encoded.append(temp)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 드라마"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████| 14019/14019 [00:47<00:00, 294.57it/s]\n"
     ]
    }
   ],
   "source": [
    "vocab_DR = {} \n",
    "DRsentences = []\n",
    "DRstop_words = set(stopwords.words('english'))\n",
    "\n",
    "for i in tqdm(DR):\n",
    "    \n",
    "    DRsentence = word_tokenize(str(i)) # 단어 토큰화를 수행합니다.\n",
    "    result = []\n",
    "\n",
    "    for word in DRsentence: \n",
    "        word = word.lower() # 모든 단어를 소문자화하여 단어의 개수를 줄입니다.\n",
    "        if word not in DRstop_words: # 단어 토큰화 된 결과에 대해서 불용어를 제거합니다.\n",
    "            if len(word) > 2: # 단어 길이가 2이하인 경우에 대하여 추가로 단어를 제거합니다.\n",
    "                result.append(word)\n",
    "                if word not in vocab_DR:\n",
    "                    vocab_DR[word] = 0 \n",
    "                vocab_DR[word] += 1\n",
    "    DRsentences.append(result) \n",
    "\n",
    "DR_encoded = []\n",
    "for s in DRsentences:\n",
    "    temp = []\n",
    "    for w in s:\n",
    "        try:\n",
    "            temp.append(all_word_to_index[w])\n",
    "        except KeyError:\n",
    "            temp.append(all_word_to_index['OOV'])\n",
    "    DR_encoded.append(temp)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 판타지"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 2727/2727 [00:14<00:00, 184.44it/s]\n"
     ]
    }
   ],
   "source": [
    "vocab_FN = {} \n",
    "FNsentences = []\n",
    "FNstop_words = set(stopwords.words('english'))\n",
    "\n",
    "for i in tqdm(FN):\n",
    "    \n",
    "    FNsentence = word_tokenize(str(i)) # 단어 토큰화를 수행합니다.\n",
    "    result = []\n",
    "\n",
    "    for word in FNsentence: \n",
    "        word = word.lower() # 모든 단어를 소문자화하여 단어의 개수를 줄입니다.\n",
    "        if word not in FNstop_words: # 단어 토큰화 된 결과에 대해서 불용어를 제거합니다.\n",
    "            if len(word) > 2: # 단어 길이가 2이하인 경우에 대하여 추가로 단어를 제거합니다.\n",
    "                result.append(word)\n",
    "                if word not in vocab_FN:\n",
    "                    vocab_FN[word] = 0 \n",
    "                vocab_FN[word] += 1\n",
    "    FNsentences.append(result) \n",
    "\n",
    "FN_encoded = []\n",
    "for s in FNsentences:\n",
    "    temp = []\n",
    "    for w in s:\n",
    "        try:\n",
    "            temp.append(all_word_to_index[w])\n",
    "        except KeyError:\n",
    "            temp.append(all_word_to_index['OOV'])\n",
    "    FN_encoded.append(temp)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 사회"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 1464/1464 [00:06<00:00, 216.19it/s]\n"
     ]
    }
   ],
   "source": [
    "vocab_SC = {} \n",
    "SCsentences = []\n",
    "SCstop_words = set(stopwords.words('english'))\n",
    "\n",
    "for i in tqdm(SC):\n",
    "    \n",
    "    SCsentence = word_tokenize(str(i)) # 단어 토큰화를 수행합니다.\n",
    "    result = []\n",
    "\n",
    "    for word in SCsentence: \n",
    "        word = word.lower() # 모든 단어를 소문자화하여 단어의 개수를 줄입니다.\n",
    "        if word not in SCstop_words: # 단어 토큰화 된 결과에 대해서 불용어를 제거합니다.\n",
    "            if len(word) > 2: # 단어 길이가 2이하인 경우에 대하여 추가로 단어를 제거합니다.\n",
    "                result.append(word)\n",
    "                if word not in vocab_SC:\n",
    "                    vocab_SC[word] = 0 \n",
    "                vocab_SC[word] += 1\n",
    "    SCsentences.append(result) \n",
    "\n",
    "SC_encoded = []\n",
    "for s in SCsentences:\n",
    "    temp = []\n",
    "    for w in s:\n",
    "        try:\n",
    "            temp.append(all_word_to_index[w])\n",
    "        except KeyError:\n",
    "            temp.append(all_word_to_index['OOV'])\n",
    "    SC_encoded.append(temp)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 뮤지컬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 307/307 [00:00<00:00, 1131.54it/s]\n"
     ]
    }
   ],
   "source": [
    "# 토큰화+전처리(3) 전체 불용어 처리\n",
    "# 전체 플롯\n",
    "from tqdm import tqdm\n",
    "Mu_vocab = {} \n",
    "Mu_sentences = []\n",
    "Mu_stop_words = set(stopwords.words('english'))\n",
    "\n",
    "for i in tqdm(Mu):\n",
    "    Mu_sentence = word_tokenize(str(i)) # 단어 토큰화를 수행합니다.\n",
    "    result = []\n",
    "    \n",
    "    for word in Mu_sentence: \n",
    "        word = word.lower() # 모든 단어를 소문자화하여 단어의 개수를 줄입니다.\n",
    "        if word not in Mu_stop_words: # 단어 토큰화 된 결과에 대해서 불용어를 제거합니다.\n",
    "            if len(word) > 2: # 단어 길이가 2이하인 경우에 대하여 추가로 단어를 제거합니다.\n",
    "                result.append(word)\n",
    "                if word not in Mu_vocab:\n",
    "                    Mu_vocab[word] = 0 \n",
    "                Mu_vocab[word] += 1\n",
    "\n",
    "    Mu_sentences.append(result) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "print(Mu_sentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "Mu_encoded = []\n",
    "for s in Mu_sentences:\n",
    "    temp = []\n",
    "    for w in s:\n",
    "        try:\n",
    "            temp.append(all_word_to_index[w])\n",
    "        except KeyError:\n",
    "            temp.append(all_word_to_index['OOV'])\n",
    "    Mu_encoded.append(temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "print(Mu_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#전처리 방법에는 NLTK의 FreqDist, 케라스(Keras) 토크나이저도 사용 가능."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "로맨스 플롯, 스릴러 따로 토큰화 해서 x train에 넣을지... 고민중\n",
    "\n",
    "이번주 : 전처리 완료, \n",
    "이번 달 목표 : 뮤지컬 장르 분류 << 다양한 모델 사용해보기.\n",
    "\n",
    "6월에 교차검증 및 장르 시각화 설계까지.\n",
    "\n",
    "다음주 :  2진분류(LSTM) 완료, RNN 분류기 만들어보기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 학습데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 영화 줄거리는 X_train에, 장르 정보는 y_train에 저장된다.\n",
    "# 테스트용 줄거리 X_test에, 테스트용 줄거리의 장르 정보는 y_test에 저장된다.\n",
    "#맞춰서 저장하기. (진행중)\n",
    "\n",
    "#X_train = train_sc_df.dropna().drop(‘trade_price_idx_value’, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = []\n",
    "Y_train = []  #0 : romance, 1 : thriller \n",
    "for i in range(train_data_size):\n",
    "    X_train.append(R_encoded[i])\n",
    "    Y_train.append([1,0])\n",
    "    X_train.append(TH_encoded[i])\n",
    "    Y_train.append([0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "줄거리 최대 길이 :  1974\n",
      "줄거리 평균 길이 :  267.093984962406\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"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "len_result = [len(s) for s in X_train]\n",
    "print(\"줄거리 최대 길이 : \",max(len_result))\n",
    "print(\"줄거리 평균 길이 : \",sum(len_result)/len(len_result))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.boxplot(len_result)\n",
    "plt.subplot(1,2,2)\n",
    "plt.hist(len_result, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 테스트 데이터\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = []\n",
    "Y_test = []  #0 : romance, 1 : thriller \n",
    "M_test = []\n",
    "for i in range(test_data_size):\n",
    "    X_test.append(R_encoded[train_data_size+i])\n",
    "    Y_test.append([1,0])\n",
    "    X_test.append(TH_encoded[train_data_size+i])\n",
    "    Y_test.append([0,1])\n",
    "M_test=Mu_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "줄거리 최대 길이 :  1749\n",
      "줄거리 평균 길이 :  197.71394395078605\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"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "len_result = [len(s) for s in X_test]\n",
    "print(\"줄거리 최대 길이 : \",max(len_result))\n",
    "print(\"줄거리 평균 길이 : \",sum(len_result)/len(len_result))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.boxplot(len_result)\n",
    "plt.subplot(1,2,2)\n",
    "plt.hist(len_result, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM 분류 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, LSTM, Embedding\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "import numpy as np\n",
    "\n",
    "M_test= np.array(M_test)\n",
    "max_len = 230\n",
    "X_train = pad_sequences(X_train, maxlen=max_len)\n",
    "X_test = pad_sequences(X_test, maxlen=max_len)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 2926 samples, validate on 2926 samples\n",
      "Epoch 1/3\n",
      "2880/2926 [============================>.] - ETA: 0s - loss: 0.6540 - acc: 0.6016\n",
      "Epoch 00001: val_acc improved from -inf to 0.66131, saving model to best_model.h5\n",
      "2926/2926 [==============================] - 28s 10ms/sample - loss: 0.6509 - acc: 0.6048 - val_loss: 0.5958 - val_acc: 0.6613\n",
      "Epoch 2/3\n",
      "2880/2926 [============================>.] - ETA: 0s - loss: 0.2971 - acc: 0.8944\n",
      "Epoch 00002: val_acc improved from 0.66131 to 0.82177, saving model to best_model.h5\n",
      "2926/2926 [==============================] - 24s 8ms/sample - loss: 0.2968 - acc: 0.8941 - val_loss: 0.4080 - val_acc: 0.8218\n",
      "Epoch 3/3\n",
      "2880/2926 [============================>.] - ETA: 0s - loss: 0.1146 - acc: 0.9672\n",
      "Epoch 00003: val_acc did not improve from 0.82177\n",
      "2926/2926 [==============================] - 24s 8ms/sample - loss: 0.1164 - acc: 0.9660 - val_loss: 0.4616 - val_acc: 0.8125\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x202d9223a48>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Embedding(15002, 120))\n",
    "model.add(LSTM(128))\n",
    "model.add(Dense(2, activation='sigmoid'))\n",
    "\n",
    "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=4)\n",
    "mc = ModelCheckpoint('best_model.h5', monitor='val_acc', mode='max', verbose=1, save_best_only=True)\n",
    "\n",
    "X_train = np.array(X_train)\n",
    "Y_train = np.array(Y_train)\n",
    "X_test = np.array(X_test)\n",
    "Y_test = np.array(Y_test)\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['acc'])\n",
    "model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=3, batch_size=64, callbacks=[es, mc])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [7.42987245e-02, 9.30592895e-01],\n",
       "       [2.09757891e-02, 9.81689215e-01],\n",
       "       [9.98074651e-01, 2.20919168e-03],\n",
       "       [6.46533668e-01, 3.48830134e-01],\n",
       "       [5.98557949e-01, 3.84560645e-01],\n",
       "       [9.99404311e-01, 7.86028046e-04],\n",
       "       [9.98620272e-01, 1.63661852e-03],\n",
       "       [9.99131620e-01, 1.08906056e-03],\n",
       "       [3.69940363e-02, 9.68174577e-01],\n",
       "       [9.98901844e-01, 1.32231705e-03],\n",
       "       [9.85736310e-01, 1.49741964e-02],\n",
       "       [6.43389583e-01, 3.48876834e-01],\n",
       "       [4.78291005e-01, 5.33889949e-01],\n",
       "       [9.99354661e-01, 8.77332001e-04],\n",
       "       [5.98759837e-02, 9.42334712e-01],\n",
       "       [9.63255167e-01, 3.59756313e-02],\n",
       "       [2.05777939e-02, 9.81795371e-01],\n",
       "       [8.92112032e-02, 9.14921701e-01],\n",
       "       [9.96331871e-01, 4.17977013e-03],\n",
       "       [5.23199812e-02, 9.52406943e-01],\n",
       "       [9.88084376e-01, 1.19793927e-02],\n",
       "       [9.69554842e-01, 2.73279194e-02],\n",
       "       [3.67223620e-01, 6.27295792e-01],\n",
       "       [9.16537941e-01, 8.18802044e-02],\n",
       "       [9.99439061e-01, 7.88773061e-04],\n",
       "       [2.48765163e-02, 9.77342725e-01],\n",
       "       [9.96949136e-01, 3.68632539e-03],\n",
       "       [6.32767797e-01, 3.68138403e-01],\n",
       "       [9.95043516e-01, 5.14345011e-03],\n",
       "       [9.93900299e-01, 6.20957604e-03],\n",
       "       [9.97990847e-01, 2.28208210e-03],\n",
       "       [8.38533640e-02, 9.23523605e-01],\n",
       "       [9.93036926e-01, 6.87335012e-03],\n",
       "       [9.56128418e-01, 4.12139818e-02],\n",
       "       [9.56128418e-01, 4.12139818e-02],\n",
       "       [1.26801789e-01, 8.76263022e-01],\n",
       "       [2.42131352e-01, 7.80837834e-01],\n",
       "       [8.39236677e-01, 1.56546742e-01],\n",
       "       [9.98509943e-01, 1.75377598e-03]], dtype=float32)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M_test = pad_sequences(M_test, maxlen=max_len)\n",
    "\n",
    "predictions = model.predict(M_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [9.97494936e-01, 2.87344446e-03],\n",
       "       [9.98973131e-01, 1.26466772e-03],\n",
       "       [4.97004330e-01, 4.97818023e-01],\n",
       "       [9.82652664e-01, 1.84881184e-02],\n",
       "       [1.31787378e-02, 9.88159657e-01],\n",
       "       [3.66922885e-01, 6.31668866e-01],\n",
       "       [9.96259332e-01, 4.08321712e-03],\n",
       "       [9.98756170e-01, 1.47667748e-03],\n",
       "       [7.72360861e-02, 9.27823484e-01],\n",
       "       [9.98449206e-01, 1.86312699e-03],\n",
       "       [3.15861739e-02, 9.71484125e-01],\n",
       "       [9.96845782e-01, 3.44880344e-03],\n",
       "       [7.42987245e-02, 9.30592895e-01],\n",
       "       [2.09757891e-02, 9.81689215e-01],\n",
       "       [9.98074651e-01, 2.20919168e-03],\n",
       "       [6.46533668e-01, 3.48830134e-01],\n",
       "       [5.98557949e-01, 3.84560645e-01],\n",
       "       [9.99404311e-01, 7.86028046e-04],\n",
       "       [9.98620272e-01, 1.63661852e-03],\n",
       "       [9.99131620e-01, 1.08906056e-03],\n",
       "       [3.69940363e-02, 9.68174577e-01],\n",
       "       [9.98901844e-01, 1.32231705e-03],\n",
       "       [9.85736310e-01, 1.49741964e-02],\n",
       "       [6.43389583e-01, 3.48876834e-01],\n",
       "       [4.78291005e-01, 5.33889949e-01],\n",
       "       [9.99354661e-01, 8.77332001e-04],\n",
       "       [5.98759837e-02, 9.42334712e-01],\n",
       "       [9.63255167e-01, 3.59756313e-02],\n",
       "       [2.05777939e-02, 9.81795371e-01],\n",
       "       [8.92112032e-02, 9.14921701e-01],\n",
       "       [9.96331871e-01, 4.17977013e-03],\n",
       "       [5.23199812e-02, 9.52406943e-01],\n",
       "       [9.88084376e-01, 1.19793927e-02],\n",
       "       [9.69554842e-01, 2.73279194e-02],\n",
       "       [3.67223620e-01, 6.27295792e-01],\n",
       "       [9.16537941e-01, 8.18802044e-02],\n",
       "       [9.99439061e-01, 7.88773061e-04],\n",
       "       [2.48765163e-02, 9.77342725e-01],\n",
       "       [9.96949136e-01, 3.68632539e-03],\n",
       "       [6.32767797e-01, 3.68138403e-01],\n",
       "       [9.95043516e-01, 5.14345011e-03],\n",
       "       [9.93900299e-01, 6.20957604e-03],\n",
       "       [9.97990847e-01, 2.28208210e-03],\n",
       "       [8.38533640e-02, 9.23523605e-01],\n",
       "       [9.93036926e-01, 6.87335012e-03],\n",
       "       [9.56128418e-01, 4.12139818e-02],\n",
       "       [9.56128418e-01, 4.12139818e-02],\n",
       "       [1.26801789e-01, 8.76263022e-01],\n",
       "       [2.42131352e-01, 7.80837834e-01],\n",
       "       [8.39236677e-01, 1.56546742e-01],\n",
       "       [9.98509943e-01, 1.75377598e-03]], dtype=float32)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "스릴러일 확률 (1)  0.2028985507246377\n",
      "로맨스일 확률 (0)  0.7971014492753623\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "predict_labels = np.argmax(predictions, axis = 1)\n",
    "th_tot = 0\n",
    "ro_tot = 0\n",
    "for i in range(69):\n",
    "    if(predict_labels[i]==1):\n",
    "        th_tot += 1\n",
    "    else:\n",
    "        ro_tot += 1\n",
    "print(\"스릴러일 확률 (1) \" , th_tot / 69)\n",
    "print(\"로맨스일 확률 (0) \" , ro_tot / 69)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 판타지 VS 사회"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "줄거리 최대 길이 :  2324\n",
      "줄거리 평균 길이 :  275.97285714285715\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"
    }
   ],
   "source": [
    "#판타지 사회\n",
    "FS_X_train = []\n",
    "FS_Y_train = [] \n",
    "\n",
    "FStrain_data_size = 1400//2\n",
    "\n",
    "for i in range(FStrain_data_size):\n",
    "    FS_X_train.append(SC_encoded[i])\n",
    "    FS_Y_train.append([1,0])\n",
    "    FS_X_train.append(FN_encoded[i])\n",
    "    FS_Y_train.append([0,1])\n",
    "    \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "len_result = [len(s) for s in FS_X_train]\n",
    "print(\"줄거리 최대 길이 : \",max(len_result))\n",
    "print(\"줄거리 평균 길이 : \",sum(len_result)/len(len_result))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.boxplot(len_result)\n",
    "plt.subplot(1,2,2)\n",
    "plt.hist(len_result, bins=50)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "줄거리 최대 길이 :  1336\n",
      "줄거리 평균 길이 :  193.3892857142857\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"
    }
   ],
   "source": [
    "FS_X_test = []\n",
    "FS_Y_test = []  #0 : romance, 1 : thriller \n",
    "\n",
    "for i in range(FStrain_data_size):\n",
    "    FS_X_test.append(SC_encoded[FStrain_data_size+i])\n",
    "    FS_Y_test.append([1,0])\n",
    "    FS_X_test.append(FN_encoded[FStrain_data_size+i])\n",
    "    FS_Y_test.append([0,1])\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "len_result = [len(s) for s in FS_X_test]\n",
    "print(\"줄거리 최대 길이 : \",max(len_result))\n",
    "print(\"줄거리 평균 길이 : \",sum(len_result)/len(len_result))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.boxplot(len_result)\n",
    "plt.subplot(1,2,2)\n",
    "plt.hist(len_result, bins=50)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_len = 230\n",
    "\n",
    "FS_X_train = pad_sequences(FS_X_train, maxlen=max_len)\n",
    "FS_X_test = pad_sequences(FS_X_test, maxlen=max_len)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1400 samples, validate on 1400 samples\n",
      "Epoch 1/3\n",
      "  64/1400 [>.............................] - ETA: 27sWARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: \n",
      "WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"
     ]
    },
    {
     "ename": "InvalidArgumentError",
     "evalue": " indices[54,195] = 8646 is not in [0, 5002)\n\t [[node sequential_6/embedding_6/embedding_lookup (defined at <ipython-input-44-4c2962189ab4>:16) ]] [Op:__inference_distributed_function_13289]\n\nErrors may have originated from an input operation.\nInput Source operations connected to node sequential_6/embedding_6/embedding_lookup:\n sequential_6/embedding_6/embedding_lookup/12144 (defined at C:\\ProgramData\\Anaconda3\\lib\\contextlib.py:112)\n\nFunction call stack:\ndistributed_function\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-44-4c2962189ab4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'binary_crossentropy'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'adam'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'acc'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFS_X_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mFS_Y_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFS_X_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mFS_Y_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m64\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmc\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    817\u001b[0m         \u001b[0mmax_queue_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    818\u001b[0m         \u001b[0mworkers\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 819\u001b[1;33m         use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[0;32m    820\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    821\u001b[0m   def evaluate(self,\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    340\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    341\u001b[0m                 \u001b[0mtraining_context\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtraining_context\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 342\u001b[1;33m                 total_epochs=epochs)\n\u001b[0m\u001b[0;32m    343\u001b[0m             \u001b[0mcbks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmake_logs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraining_result\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mrun_one_epoch\u001b[1;34m(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)\u001b[0m\n\u001b[0;32m    126\u001b[0m         step=step, mode=mode, size=current_batch_size) as batch_logs:\n\u001b[0;32m    127\u001b[0m       \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 128\u001b[1;33m         \u001b[0mbatch_outs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    129\u001b[0m       \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mStopIteration\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOutOfRangeError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m         \u001b[1;31m# TODO(kaftan): File bug about tf function and errors.OutOfRangeError?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2_utils.py\u001b[0m in \u001b[0;36mexecution_function\u001b[1;34m(input_fn)\u001b[0m\n\u001b[0;32m     96\u001b[0m     \u001b[1;31m# `numpy` translates Tensors to values in Eager mode.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     97\u001b[0m     return nest.map_structure(_non_none_constant_value,\n\u001b[1;32m---> 98\u001b[1;33m                               distributed_function(input_fn))\n\u001b[0m\u001b[0;32m     99\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    100\u001b[0m   \u001b[1;32mreturn\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    566\u001b[0m         \u001b[0mxla_context\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    567\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 568\u001b[1;33m       \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    569\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    570\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    630\u001b[0m         \u001b[1;31m# Lifting succeeded, so variables are initialized and we can run the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    631\u001b[0m         \u001b[1;31m# stateless function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 632\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    633\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    634\u001b[0m       \u001b[0mcanon_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcanon_kwds\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2361\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2362\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2363\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2364\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2365\u001b[0m   \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[1;34m(self, args, kwargs)\u001b[0m\n\u001b[0;32m   1609\u001b[0m          if isinstance(t, (ops.Tensor,\n\u001b[0;32m   1610\u001b[0m                            resource_variable_ops.BaseResourceVariable))),\n\u001b[1;32m-> 1611\u001b[1;33m         self.captured_inputs)\n\u001b[0m\u001b[0;32m   1612\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1613\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1690\u001b[0m       \u001b[1;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1691\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[1;32m-> 1692\u001b[1;33m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[0;32m   1693\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[0;32m   1694\u001b[0m         \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    543\u001b[0m               \u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    544\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"executor_type\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexecutor_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"config_proto\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 545\u001b[1;33m               ctx=ctx)\n\u001b[0m\u001b[0;32m    546\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    547\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     65\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     66\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 67\u001b[1;33m     \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     68\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m     keras_symbolic_tensors = [\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\six.py\u001b[0m in \u001b[0;36mraise_from\u001b[1;34m(value, from_value)\u001b[0m\n",
      "\u001b[1;31mInvalidArgumentError\u001b[0m:  indices[54,195] = 8646 is not in [0, 5002)\n\t [[node sequential_6/embedding_6/embedding_lookup (defined at <ipython-input-44-4c2962189ab4>:16) ]] [Op:__inference_distributed_function_13289]\n\nErrors may have originated from an input operation.\nInput Source operations connected to node sequential_6/embedding_6/embedding_lookup:\n sequential_6/embedding_6/embedding_lookup/12144 (defined at C:\\ProgramData\\Anaconda3\\lib\\contextlib.py:112)\n\nFunction call stack:\ndistributed_function\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Embedding(5002, 120))\n",
    "model.add(LSTM(120))\n",
    "model.add(Dense(2, activation='sigmoid'))\n",
    "\n",
    "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=4)\n",
    "mc = ModelCheckpoint('best_model.h5', monitor='val_acc', mode='max', verbose=1, save_best_only=True)\n",
    "\n",
    "FS_X_train = np.array(FS_X_train)\n",
    "FS_X_test = np.array(FS_X_test)\n",
    "FS_Y_train = np.array(FS_Y_train)\n",
    "FS_Y_test = np.array(FS_Y_test)\n",
    "\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['acc'])\n",
    "model.fit(FS_X_train, FS_Y_train, validation_data=(FS_X_test, FS_Y_test), epochs=3, batch_size=64, callbacks=[es, mc])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 위에서 실행한 베스트 모델로 뮤지컬 예측값 확인하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 드라마 VS 역사"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "줄거리 최대 길이 :  1389\n",
      "줄거리 평균 길이 :  258.6014285714286\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"
    }
   ],
   "source": [
    "\n",
    "DH_X_train = []\n",
    "DH_Y_train = [] \n",
    "\n",
    "DHtrain_data_size = 1400//2\n",
    "\n",
    "for i in range(DHtrain_data_size):\n",
    "    DH_X_train.append(DR_encoded[i])\n",
    "    DH_Y_train.append([1,0])\n",
    "    DH_X_train.append(HS_encoded[i])\n",
    "    DH_Y_train.append([0,1])\n",
    "    \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "len_result = [len(s) for s in DH_X_train]\n",
    "print(\"줄거리 최대 길이 : \",max(len_result))\n",
    "print(\"줄거리 평균 길이 : \",sum(len_result)/len(len_result))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.boxplot(len_result)\n",
    "plt.subplot(1,2,2)\n",
    "plt.hist(len_result, bins=50)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "줄거리 최대 길이 :  1880\n",
      "줄거리 평균 길이 :  183.68142857142857\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"
    }
   ],
   "source": [
    "DH_X_test = []\n",
    "DH_Y_test = []  \n",
    "\n",
    "for i in range(DHtrain_data_size):\n",
    "    DH_X_test.append(DR_encoded[DHtrain_data_size+i])\n",
    "    DH_Y_test.append([1,0])\n",
    "    DH_X_test.append(HS_encoded[DHtrain_data_size+i])\n",
    "    DH_Y_test.append([0,1])\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "len_result = [len(s) for s in DH_X_test]\n",
    "print(\"줄거리 최대 길이 : \",max(len_result))\n",
    "print(\"줄거리 평균 길이 : \",sum(len_result)/len(len_result))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.boxplot(len_result)\n",
    "plt.subplot(1,2,2)\n",
    "plt.hist(len_result, bins=50)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1400 samples, validate on 1400 samples\n",
      "Epoch 1/3\n",
      "  64/1400 [>.............................] - ETA: 31sWARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: \n",
      "WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"
     ]
    },
    {
     "ename": "InvalidArgumentError",
     "evalue": " indices[46,167] = 15001 is not in [0, 5002)\n\t [[node sequential_9/embedding_9/embedding_lookup (defined at <ipython-input-49-edff8fe77726>:22) ]] [Op:__inference_distributed_function_17701]\n\nErrors may have originated from an input operation.\nInput Source operations connected to node sequential_9/embedding_9/embedding_lookup:\n sequential_9/embedding_9/embedding_lookup/16556 (defined at C:\\ProgramData\\Anaconda3\\lib\\contextlib.py:112)\n\nFunction call stack:\ndistributed_function\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-49-edff8fe77726>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'binary_crossentropy'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'adam'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'acc'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDH_X_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDH_Y_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDH_X_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDH_Y_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m64\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmc\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    817\u001b[0m         \u001b[0mmax_queue_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    818\u001b[0m         \u001b[0mworkers\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 819\u001b[1;33m         use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[0;32m    820\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    821\u001b[0m   def evaluate(self,\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    340\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    341\u001b[0m                 \u001b[0mtraining_context\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtraining_context\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 342\u001b[1;33m                 total_epochs=epochs)\n\u001b[0m\u001b[0;32m    343\u001b[0m             \u001b[0mcbks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmake_logs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraining_result\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mrun_one_epoch\u001b[1;34m(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)\u001b[0m\n\u001b[0;32m    126\u001b[0m         step=step, mode=mode, size=current_batch_size) as batch_logs:\n\u001b[0;32m    127\u001b[0m       \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 128\u001b[1;33m         \u001b[0mbatch_outs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    129\u001b[0m       \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mStopIteration\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOutOfRangeError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m         \u001b[1;31m# TODO(kaftan): File bug about tf function and errors.OutOfRangeError?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2_utils.py\u001b[0m in \u001b[0;36mexecution_function\u001b[1;34m(input_fn)\u001b[0m\n\u001b[0;32m     96\u001b[0m     \u001b[1;31m# `numpy` translates Tensors to values in Eager mode.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     97\u001b[0m     return nest.map_structure(_non_none_constant_value,\n\u001b[1;32m---> 98\u001b[1;33m                               distributed_function(input_fn))\n\u001b[0m\u001b[0;32m     99\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    100\u001b[0m   \u001b[1;32mreturn\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    566\u001b[0m         \u001b[0mxla_context\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    567\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 568\u001b[1;33m       \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    569\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    570\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    630\u001b[0m         \u001b[1;31m# Lifting succeeded, so variables are initialized and we can run the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    631\u001b[0m         \u001b[1;31m# stateless function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 632\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    633\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    634\u001b[0m       \u001b[0mcanon_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcanon_kwds\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2361\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2362\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2363\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2364\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2365\u001b[0m   \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[1;34m(self, args, kwargs)\u001b[0m\n\u001b[0;32m   1609\u001b[0m          if isinstance(t, (ops.Tensor,\n\u001b[0;32m   1610\u001b[0m                            resource_variable_ops.BaseResourceVariable))),\n\u001b[1;32m-> 1611\u001b[1;33m         self.captured_inputs)\n\u001b[0m\u001b[0;32m   1612\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1613\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1690\u001b[0m       \u001b[1;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1691\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[1;32m-> 1692\u001b[1;33m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[0;32m   1693\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[0;32m   1694\u001b[0m         \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    543\u001b[0m               \u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    544\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"executor_type\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexecutor_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"config_proto\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 545\u001b[1;33m               ctx=ctx)\n\u001b[0m\u001b[0;32m    546\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    547\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     65\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     66\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 67\u001b[1;33m     \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     68\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m     keras_symbolic_tensors = [\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\six.py\u001b[0m in \u001b[0;36mraise_from\u001b[1;34m(value, from_value)\u001b[0m\n",
      "\u001b[1;31mInvalidArgumentError\u001b[0m:  indices[46,167] = 15001 is not in [0, 5002)\n\t [[node sequential_9/embedding_9/embedding_lookup (defined at <ipython-input-49-edff8fe77726>:22) ]] [Op:__inference_distributed_function_17701]\n\nErrors may have originated from an input operation.\nInput Source operations connected to node sequential_9/embedding_9/embedding_lookup:\n sequential_9/embedding_9/embedding_lookup/16556 (defined at C:\\ProgramData\\Anaconda3\\lib\\contextlib.py:112)\n\nFunction call stack:\ndistributed_function\n"
     ]
    }
   ],
   "source": [
    "max_len = 200\n",
    "\n",
    "DH_X_train = pad_sequences(DH_X_train, maxlen=max_len)\n",
    "DH_X_test = pad_sequences(DH_X_test, maxlen=max_len)\n",
    "\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Embedding(5002, 120))\n",
    "model.add(LSTM(120))\n",
    "model.add(Dense(2, activation='sigmoid'))\n",
    "\n",
    "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=4)\n",
    "mc = ModelCheckpoint('best_model.h5', monitor='val_acc', mode='max', verbose=1, save_best_only=True)\n",
    "\n",
    "DH_X_train = np.array(DH_X_train)\n",
    "DH_X_test = np.array(DH_X_test)\n",
    "DH_Y_train = np.array(DH_Y_train)\n",
    "DH_Y_test = np.array(DH_Y_test)\n",
    "\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['acc'])\n",
    "model.fit(DH_X_train, DH_Y_train, validation_data=(DH_X_test, DH_Y_test), epochs=3, batch_size=64, callbacks=[es, mc])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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