readme file

@@ -27,26 +27,134 @@
 - 장르 시각화 : matplot, Seaborn 패키지를 통한 시각화
 - 장르 시각화 : matplot, Seaborn 패키지를 통한 시각화
 
 
 ## Model
 ## Model
-- 데이터 수집 : [CMU Movie Summary Corpus][CMU]의 Detaset  42,306개, 뮤지컬 줄거리 255개
+- 데이터 수집 : [CMU Movie Summary Corpus][CMU]의 Detaset  42,306개, 뮤지컬 줄거리 307개
 - 데이터 전처리 : Detaset 토큰화, 불용어 처리 후 정수 인코딩
 - 데이터 전처리 : Detaset 토큰화, 불용어 처리 후 정수 인코딩
-- 데이터 분석 :  의사결정나무 모델, RNN, k-nearest neighbor < 연구중
+- 데이터 분석 :  lstm
 - 데이터 검증 : k-fold 교차검증을 활용하여 장르별 정확도를 확인
 - 데이터 검증 : k-fold 교차검증을 활용하여 장르별 정확도를 확인
-- 데이터 시각화 : 분석된 장르를 장르 단어 분포도로 시각화한다.
-
-
-
-## Schedule
-
-__3월__
-  - 주제 선정   
-     
-__4월__
-  - 뮤지컬 줄거리 웹 크롤링
-  - 테스트 데이터 라벨링   
-     
-__5월__
-  - 데이터 전처리
-    + 토큰화 (완료)
-    + 라벨링 (진행중)
-
-
+- 데이터 시각화 : 분석된 뮤지컬 데이터를 장르 단위로 시각화한다.
+
+## 과제 수행
+### 데이터 수집
+[ 장르 분류 모델 학습 데이터 ] 
+ 모델 학습을 위한 데이터는 뮤지컬 줄거리로는 양이 충분하지 못해 뮤지컬과 줄거리가 비슷한 영화 데이 터를 활용했습니다. http://www.cs.cmu.edu/~ark/personas/에서 영화 줄거리(영어) 데이터를 42,306개 수집했 습니다.  
+ 
+ 
+* 수집한 영화 줄거리 태깅 일부 : 각 영화 줄거리에는 데이터 제공 사이트에서 미리 장르를 다양하게 태깅 된 상태였습니다.  
+ 
+ 
+ 
+ 미리 태깅되어 있는 장르를 [‘romance’, ’fantasy’ ,‘thriller’, ‘drama’, ’ history’ , ‘social’ , ‘#N/A’] 같이 6개의 장르로 재분류했습니다.  
+
+
+
+ 재분류 방법은 위 표처럼 직접 모든(364개) 장르를 확인하며 위 6가지 정해놓은 장르 중 가장 유사한 장르 를 선택했습니다. 장르 분류가 애매한 경우는 ‘#N/A’으로 처리했습니다. 이러한 방법을 364개의 장르를 재 태깅했습니다.  
+
+재태깅된 장르를 기반으로 줄거리를 6가지 장르로 분류해서 장르별 csv파일로 추출했습니다
+
+
+[ 모델 테스트 데이터 ] 
+ 브로드웨이 뮤지컬 줄거리를 크롤링을 통해 수집했습니다. 크롤링한 웹사이트는 https://broadwaymusicalhome.com/shows.htm 입니다. 총 307건의 줄거리를 크롤링하였습니다. (Beautifulsoup 패키지를 사용했습니다) 
+
+### 데이터 전처리
+
+[ NLTK 라이브러리 활용 전처리 ] 
+ 장르가 NULL인 줄거리를 제외하고 약 35,000개의 줄거리 데이터 전처리를 진행했습니다. 이때단어 토큰화, 불용어 제거를 위해 NLTK 자연어처리 라이브러리를 사용했습니다.  
+* 전처리 소스코드 일부 (자세한 내용은 주석처리했습니다.) 
+ 
+ 
+  ```from tqdm import tqdm
+  all_vocab = {} 
+  all_sentences = []
+  stop_words = set(stopwords.words('english'))
+  
+  for i in tqdm(allplot):
+      all_sentences = word_tokenize(str(i)) # 단어 토큰화를 수행합니다.
+      result = []
+      for word in all_sentences: 
+          word = word.lower() # 모든 단어를 소문자화하여 단어의 개수를 줄입니다.
+          if word not in stop_words: # 단어 토큰화 된 결과에 대해서 불용어를 제거합니다.
+              if len(word) > 2: # 단어 길이가 2이하인 경우에 대하여 추가로 단어를 제거합니다.
+                  result.append(word)
+                  if word not in all_vocab:
+                      all_vocab[word] = 0 
+                  all_vocab[word] += 1
+      all_sentences.append(result) 
+    
+  all_vocab_sorted = sorted(all_vocab.items(), key = lambda x:x[1], reverse = True)
+
+  all_word_to_index = {}
+  i=0
+  for (word, frequency) in all_vocab_sorted :
+      if frequency > 1 : # 빈도수가 적은 단어는 제외한다.
+          i=i+1
+          all_word_to_index[word] = i
+  vocab_size = 15000 #상위 15000개 단어만 사용
+  words_frequency = [w for w,c in all_word_to_index.items() if c >= vocab_size + 1] # 인덱스가 15000 초과인 단어 제거
+  for w in words_frequency:
+      del all_word_to_index[w] # 해당 단어에 대한 인덱스 정보를 삭제  
+  all_word_to_index['OOV'] = len(all_word_to_index) + 1
+  ```
+[ 전체 줄거리(전체 : 로맨스, 판타지, 스릴러, 역사 줄거리) 단어 인덱스 부여 ] 
+ 단어 사용 빈도수가 적은 단어는 제외하기 위해서 빈도순으로 인덱스를 부여했습니다. 15,000순위 미만인 단어 정보는 삭제했습니다.  
+  
+
+ 
+[ 학습 데이터(= RM_train+TH_train+FN_train+HS_train ) 인코딩 ]  
+ 총 학습 데이터는 7000개로 ‘로맨스 : 스릴러 : 판타지 : 역사 = 2 : 2 : 2 : 1’의 비율로 맞추었습니다.  
+* 인덱스 부여 소스코드 일부 (자세한 내용은 주석 처리했습니다.) 
+### 모델 학습
+
+다중 분류를 위해 순환 신경망 모델을 사용했습니다.  
+* LSTM 모델 구현 소스코드 (자세한 설명은 주석 처리했습니다.) 
+```from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, LSTM, Embedding
+from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
+import numpy as np
+
+
+M_test=Mu_encoded
+M_test= np.array(M_test)
+max_len = 230
+X_train = pad_sequences(X_train, maxlen=max_len)
+X_test = pad_sequences(X_test, maxlen=max_len)
+
+model = Sequential()
+model.add(Embedding(15002, 120))
+model.add(LSTM(128))
+model.add(Dense(4, activation='softmax'))
+
+es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=4)
+mc = ModelCheckpoint('best_model.h5', monitor='val_acc', mode='max', verbose=1, save_best_only=True)
+
+X_train = np.array(X_train)
+Y_train = np.array(Y_train)
+X_test = np.array(X_test)
+Y_test = np.array(Y_test)
+
+model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['acc'])
+model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=7, batch_size=64, callbacks=[es, mc])
+```
+    
+ 뮤지컬 데이터로 테스트 결과 많은 뮤지컬 작품이 로맨스로 분류된 것을 확인하였고, 스릴러가 가장 적은 비율로 분류된것을 확인했습니 다.  
+[ 실제 줄거리와 비교 ] 
+* 오페라의 유령 뮤지컬 줄거리의 장르를 예측한 결과 아래와 같이 로맨스로 정확하게 분류되었습니다. 오 페라의 유령같이 장르 특징이 뚜렷한 줄거리의 경우는 항상 정확한 분류가 이루어지는 것을 확인했습니다. 
+
+## 기대효과 및 활용방안 
+공식 티켓 판매처(인터파크, 예스 24공연 등)에도 뮤지컬 장르구분은 존재하지 않았습니다. 이런 사이트에서 축적된 공연 데이터를 통해 더 정확한 장르 구분을 한다면 일반인들의 뮤지컬에 대한 관심과 접근성을 높 일 수 있을것으로 기대합니다. 또한, 국내 창작 뮤지컬에 로맨스 뮤지컬 뿐만 아니라 다양한 장르가 존재함 을 알릴 수 있을 것입니다. 또한, 분류된 결과를 데이터베이스에 구축하고 웹사이트를 구현하면 뮤지컬 장 르 체계화에 도움이 될 것입니다. 
+
+## 결론 및 제언 
+이진 분류의 경우(예시- 로맨스 vs 판타지)에는 테스트이렇 데이터 정확도가 80%에 이르며 높은 정확도를 보였습니다. 하지만 다중 분류(4개)의 경우에 학습한 경우 정확도가 최대 63%까지만 이르며 모델의 성능을 높이지 못했습니다. 원인으로 첫째, 학습한 줄거리 데이터의 길이가 짧아 내용이 불충분한 경우가 있습니다. 둘째, 줄거리 길이는 충분하지만 분류를 위한 특성을 추출하기 어려운 단어가 많은 경우가 있었습니다.  
+ 
+프로젝트 진행 초반에는 한글 줄거리로 국내 뮤지컬 장르를 분류하고자 했습니다. 하지만 국내 뮤지컬 줄 거리의 대부분은 홍보용으로 내용에 대한 설명이 부실한 경우가 많아 분석에 어려움이 있었습니다. 충분한 한국어 뮤지컬 줄거리 데이터를 구하지 못해 영어 자연어처리로 주제가 변경된 부분이 아쉽습니다
+
+###  역할 분담 
+양윤지 : 
+ 영화 데이터 수집&정리, 줄거리 데이터 장르 재태깅, Train,Test데이터 시각화&패딩, LSTM모델구현, 시각화 
+ 
+ 
+김서영 :  
+뮤지컬 데이터 수집&정리, 장르 데이터 전처리, Train,Test데이터 인코딩, LSTM모델로 학습, 테스트 데이터 학
+습, 뮤지컬 데이터 분류,  
+ 
+시연 영상 링크 : https://youtu.be/EKjjQ0tHM4s

-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# <4월 20일 ~ 4월 24일>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "(1) 플레이DB 웹크롤링 -> 에러처리, csv파일로 옮기기 남음\n",
-    "\n",
-    "(2) **tf-idf**로 키워드 추출 고려 <br/>\n",
-    " -> 줄거리가 짧아서 단어 빈도수가 커야 6정도 ==> 따라서 장르별 대표작들 줄거리를 모아서 해보기로 계획\n",
-    "\n",
-    "(3) **word2vec**로 문맥이 비슷한 단어 파악하려했지만<br/>\n",
-    " ->학습 시간이 오래걸리는 문제 <br/>\n",
-    " ->단어 관계 파악 후에 어떻게 처리할지 고민,,<br/>\n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# <다음주 계획>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "-장르별 키워드 생각(크롤링x, 생각대로) <br/>\n",
-    "ex) 로맨스 = ['사랑','연인','애틋',........]<br/>\n",
-    "-가볍게 테스트<br/>\n",
-    "\n",
-    "\n",
-    "### 테스트 결과가 잘 나온다면,, 장르별 키워드 추출 모델 만들기\n",
-    "#### (잘 나오지 못하면,, 새로운 방법 고려)\n",
-    "---\n",
-    "\n",
-    "(1-1) **불용어 대표 사전 제작**<br/>\n",
-    "\n",
-    "- 뮤지컬정보관련된거 ('뮤지컬','제작사','콘텐츠', 뮤지컬이름, '창작','티켓', '매진'....)<br/>\n",
-    "- 고유 명사<br/>\n",
-    "- 시간관련 명사, 부사 모든것, (시작 ,나이, '최초','전날' '작년', OOOO년 OO월 OO일, '당시')<br/>\n",
-    "- 장소는 애매,, ('감옥'이런 장소는 유의미,,)(그런데 지명은 무의미)<br/>\n",
-    "- 감정이 아닌 단어 예를 들면 ('표현', '세상',...) <br/>\n",
-    "- 흔한 불용어('우리' ,,)<br/>\n",
-    "- 한국어가 아닌 단어(한자, 영어)<br/>\n",
-    "\n",
-    "(1-2) **장르 대표작 30편씩 고르기**<br/>\n",
-    "=> 장르별 대표작 30편씩은 줄거리 + 그 이외 기사, 대본 가능한대로 긁어와..<br/>\n",
-    "\n",
-    "*키워드 추출은 불용어 사전을 통해 불필요한 단어를 제외하고, 나머지 단어들의 사용된 빈도를 기준으로함.<br/>\n",
-    "*이때 키워드를 사전 api와 비교해서 나오지 않는것은 이름으로 간주하고 제거.<br/>\n",
-    "\n",
-    "(1-3) 장르 대표작들을 통해 **‘장르별 대표 키워드 사전 만들기’** => 이때 키워드가 포함된 문장도 추출<br/>\n",
-    "(왜냐하면 word2vec을 사용하려면 문장 필요)<br/>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. 불용어 처리\n",
-    "## 2. 어근 추출\n",
-    "## 3. 인코딩\n",
-    "## 4. 패딩 \n",
-    "\n",
-    "## 5. "
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

-{
- "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",
-    "# /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",
-    "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": 27,
-   "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",
-    "   "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "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"
-     ]
-    }
-   ],
-   "source": [
-    "print(len(allplot))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 모든 장르 줄거리 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████████████████████████████████████████████████████████████████| 35230/35230 [04:06<00:00, 142.67it/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": 30,
-   "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": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|█████████████████████████████████████████████████████████████████████████████| 5699/5699 [00:36<00:00, 154.26it/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": 32,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|█████████████████████████████████████████████████████████████████████████████| 9823/9823 [01:19<00:00, 124.04it/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": "code",
-   "execution_count": null,
-   "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": null,
-   "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": 33,
-   "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(0)\n",
-    "    X_train.append(TH_encoded[i])\n",
-    "    Y_train.append(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#print(X_train[2])\n",
-    "#print(Y_train[2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "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": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_test = []\n",
-    "Y_test = []  #0 : romance, 1 : thriller \n",
-    "for i in range(test_data_size):\n",
-    "    X_test.append(R_encoded[train_data_size+i])\n",
-    "    Y_test.append(0)\n",
-    "    X_test.append(TH_encoded[train_data_size+i])\n",
-    "    Y_test.append(1)\n",
-    "    \n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "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": 37,
-   "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",
-    "max_len = 600\n",
-    "X_train = pad_sequences(X_train, maxlen=max_len)\n",
-    "X_test = pad_sequences(X_train, maxlen=max_len)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train on 2926 samples, validate on 2926 samples\n",
-      "Epoch 1/3\n",
-      "2880/2926 [============================>.] - ETA: 1s - loss: 0.6089 - acc: 0.6573\n",
-      "Epoch 00001: val_acc improved from -inf to 0.80554, saving model to best_model.h5\n",
-      "2926/2926 [==============================] - 108s 37ms/sample - loss: 0.6094 - acc: 0.6582 - val_loss: 0.6007 - val_acc: 0.8055\n",
-      "Epoch 2/3\n",
-      "2880/2926 [============================>.] - ETA: 1s - loss: 0.4706 - acc: 0.8271\n",
-      "Epoch 00002: val_acc improved from 0.80554 to 0.93233, saving model to best_model.h5\n",
-      "2926/2926 [==============================] - 103s 35ms/sample - loss: 0.4674 - acc: 0.8284 - val_loss: 0.1962 - val_acc: 0.9323\n",
-      "Epoch 3/3\n",
-      "2048/2926 [===================>..........] - ETA: 25s - loss: 0.1774 - acc: 0.9404"
-     ]
-    }
-   ],
-   "source": [
-    "model = Sequential()\n",
-    "model.add(Embedding(15002, 120))\n",
-    "model.add(LSTM(128))\n",
-    "model.add(Dense(1, 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": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'load_model' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-22-d9b5bb19b28c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mloaded_model\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mload_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'best_model.h5'\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      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\n 테스트 정확도: %.4f\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mloaded_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\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;31mNameError\u001b[0m: name 'load_model' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "loaded_model = load_model('best_model.h5')\n",
-    "print(\"\\n 테스트 정확도: %.4f\" % (loaded_model.evaluate(X_test, Y_test)[1]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n",
-      "11493376/11490434 [==============================] - 6s 1us/step\n",
-      "WARNING:tensorflow:From /Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Colocations handled automatically by placer.\n",
-      "WARNING:tensorflow:From /Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use tf.cast instead.\n",
-      "Train on 18000 samples, validate on 42000 samples\n",
-      "Epoch 1/5\n",
-      "18000/18000 [==============================] - 3s 181us/step - loss: 1.1402 - acc: 0.7294 - val_loss: 0.6392 - val_acc: 0.8463\n",
-      "Epoch 2/5\n",
-      "18000/18000 [==============================] - 3s 170us/step - loss: 0.5055 - acc: 0.8734 - val_loss: 0.4687 - val_acc: 0.8739\n",
-      "Epoch 3/5\n",
-      "18000/18000 [==============================] - 3s 153us/step - loss: 0.4080 - acc: 0.8901 - val_loss: 0.4105 - val_acc: 0.8872\n",
-      "Epoch 4/5\n",
-      "18000/18000 [==============================] - 3s 148us/step - loss: 0.3652 - acc: 0.8979 - val_loss: 0.3805 - val_acc: 0.8939\n",
-      "Epoch 5/5\n",
-      "18000/18000 [==============================] - 3s 156us/step - loss: 0.3390 - acc: 0.9049 - val_loss: 0.3642 - val_acc: 0.8976\n",
-      "10000/10000 [==============================] - 0s 28us/step\n",
-      "\n",
-      "loss_and_metrics : [0.33921422773599624, 0.9048]\n",
-      "True : 7, Predict : 7\n",
-      "True : 1, Predict : 1\n",
-      "True : 1, Predict : 1\n",
-      "True : 7, Predict : 7\n",
-      "True : 9, Predict : 9\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 영화 리뷰 예제\n",
-    "# 0. 사용할 패키지 불러오기\n",
-    "from keras.utils import np_utils\n",
-    "from keras.datasets import mnist\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers import Dense, Activation\n",
-    "import numpy as np\n",
-    "from numpy import argmax\n",
-    "\n",
-    "# 1. 데이터셋 생성하기\n",
-    "\n",
-    "# 훈련셋과 시험셋 불러오기\n",
-    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
-    "\n",
-    "# 데이터셋 전처리\n",
-    "x_train = x_train.reshape(60000, 784).astype('float32') / 255.0\n",
-    "x_test = x_test.reshape(10000, 784).astype('float32') / 255.0\n",
-    "\n",
-    "# 원핫인코딩 (one-hot encoding) 처리\n",
-    "y_train = np_utils.to_categorical(y_train)\n",
-    "y_test = np_utils.to_categorical(y_test)\n",
-    "\n",
-    "# 훈련셋과 검증셋 분리\n",
-    "x_val = x_train[:42000] # 훈련셋의 30%를 검증셋으로 사용\n",
-    "x_train = x_train[42000:]\n",
-    "y_val = y_train[:42000] # 훈련셋의 30%를 검증셋으로 사용\n",
-    "y_train = y_train[42000:]\n",
-    "\n",
-    "# 2. 모델 구성하기\n",
-    "model = Sequential()\n",
-    "model.add(Dense(units=64, input_dim=28*28, activation='relu'))\n",
-    "model.add(Dense(units=10, activation='softmax'))\n",
-    "\n",
-    "# 3. 모델 학습과정 설정하기\n",
-    "model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n",
-    "\n",
-    "# 4. 모델 학습시키기\n",
-    "model.fit(x_train, y_train, epochs=5, batch_size=32, validation_data=(x_val, y_val))\n",
-    "\n",
-    "# 5. 모델 평가하기\n",
-    "loss_and_metrics = model.evaluate(x_test, y_test, batch_size=32)\n",
-    "print('')\n",
-    "print('loss_and_metrics : ' + str(loss_and_metrics))\n",
-    "\n",
-    "# 6. 모델 사용하기\n",
-    "xhat_idx = np.random.choice(x_test.shape[0], 5)\n",
-    "xhat = x_test[xhat_idx]\n",
-    "yhat = model.predict_classes(xhat)\n",
-    "\n",
-    "for i in range(5):\n",
-    "    print('True : ' + str(argmax(y_test[xhat_idx[i]])) + ', Predict : ' + str(yhat[i]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
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