[E-06]Project_RNN_Sentiment_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "understood-player",
   "metadata": {},
   "source": [
    "# 6-11. 프로젝트 : 네이버 영화리뷰 감성분석 도전하기\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "congressional-error",
   "metadata": {},
   "source": [
    "## # 데이터 준비와 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "awful-hawaiian",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>document</th>\n",
       "      <th>label</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9976970</td>\n",
       "      <td>아 더빙.. 진짜 짜증나네요 목소리</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3819312</td>\n",
       "      <td>흠...포스터보고 초딩영화줄....오버연기조차 가볍지 않구나</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10265843</td>\n",
       "      <td>너무재밓었다그래서보는것을추천한다</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9045019</td>\n",
       "      <td>교도소 이야기구먼 ..솔직히 재미는 없다..평점 조정</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6483659</td>\n",
       "      <td>사이몬페그의 익살스런 연기가 돋보였던 영화!스파이더맨에서 늙어보이기만 했던 커스틴 ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      "text/plain": [
       "         id                                           document  label\n",
       "0   9976970                                아 더빙.. 진짜 짜증나네요 목소리      0\n",
       "1   3819312                  흠...포스터보고 초딩영화줄....오버연기조차 가볍지 않구나      1\n",
       "2  10265843                                  너무재밓었다그래서보는것을추천한다      0\n",
       "3   9045019                      교도소 이야기구먼 ..솔직히 재미는 없다..평점 조정      0\n",
       "4   6483659  사이몬페그의 익살스런 연기가 돋보였던 영화!스파이더맨에서 늙어보이기만 했던 커스틴 ...      1"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 데이터를 읽어봅시다. \n",
    "train_data = pd.read_table('~/aiffel/sentiment_classification/data/ratings_train.txt')\n",
    "test_data = pd.read_table('~/aiffel/sentiment_classification/data/ratings_test.txt')\n",
    "\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "spanish-protest",
   "metadata": {},
   "source": [
    "## # 데이터로더 구성\n",
    "\n",
    "- 데이터의 중복 제거\n",
    "- NaN 결측치 제거\n",
    "- 한국어 토크나이저로 토큰화\n",
    "- 불용어(Stopwords) 제거\n",
    "- 사전word_to_index 구성\n",
    "- 텍스트 스트링을 사전 인덱스 스트링으로 변환\n",
    "- X_train, y_train, X_test, y_test, word_to_index 리턴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "confident-combine",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "mecab-0.996-ko-0.9.2/src/\n",
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      "mecab-0.996-ko-0.9.2/src/learner_node.h\n",
      "mecab-0.996-ko-0.9.2/src/mecab-dict-gen.cpp\n",
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      "mecab-0.996-ko-0.9.2/src/winmain.h\n",
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      "mecab-0.996-ko-0.9.2/src/common.h\n",
      "mecab-0.996-ko-0.9.2/src/dictionary_rewriter.cpp\n",
      "mecab-0.996-ko-0.9.2/src/Makefile.msvc.in\n",
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      "mecab-0.996-ko-0.9.2/src/feature_index.h\n",
      "mecab-0.996-ko-0.9.2/src/iconv_utils.cpp\n",
      "mecab-0.996-ko-0.9.2/src/char_property.cpp\n",
      "mecab-0.996-ko-0.9.2/src/mecab-test-gen.cpp\n",
      "mecab-0.996-ko-0.9.2/src/tagger.cpp\n",
      "mecab-0.996-ko-0.9.2/src/mecab-cost-train.cpp\n",
      "mecab-0.996-ko-0.9.2/src/learner.cpp\n",
      "mecab-0.996-ko-0.9.2/src/dictionary.cpp\n",
      "mecab-0.996-ko-0.9.2/src/lbfgs.cpp\n",
      "mecab-0.996-ko-0.9.2/src/ucs.h\n",
      "mecab-0.996-ko-0.9.2/src/writer.cpp\n",
      "mecab-0.996-ko-0.9.2/src/learner_tagger.cpp\n",
      "mecab-0.996-ko-0.9.2/src/lbfgs.h\n",
      "mecab-0.996-ko-0.9.2/src/libmecab.cpp\n",
      "mecab-0.996-ko-0.9.2/src/tokenizer.h\n",
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      "mecab-0.996-ko-0.9.2/src/utils.cpp\n",
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      "mecab-0.996-ko-0.9.2/src/param.cpp\n",
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      "mecab-0.996-ko-0.9.2/src/mmap.h\n",
      "mecab-0.996-ko-0.9.2/src/viterbi.h\n",
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      "mecab-0.996-ko-0.9.2/Makefile.in\n",
      "checking for a BSD-compatible install... /usr/bin/install -c\n",
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      "checking for gcc option to accept ISO C89... none needed\n",
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      "checking dependency style of gcc... none\n",
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      "checking how to run the C preprocessor... gcc -E\n",
      "checking for grep that handles long lines and -e... /bin/grep\n",
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      "checking build system type... x86_64-unknown-linux-gnu\n",
      "checking host system type... x86_64-unknown-linux-gnu\n",
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      "checking if the linker (/usr/bin/ld) is GNU ld... yes\n",
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      "checking how to convert x86_64-unknown-linux-gnu file names to x86_64-unknown-linux-gnu format... func_convert_file_noop\n",
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      "checking command to parse /usr/bin/nm -B output from gcc object... ok\n",
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      "         extern size_t iconv (iconv_t cd, char * *inbuf, size_t *inbytesleft, char * *outbuf, size_t *outbytesleft);\n",
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      "checking if g++ supports OSX native atomic operations (optional)... no\n",
      "checking if g++ environment provides all required features... yes\n",
      "configure: creating ./config.status\n",
      "config.status: creating Makefile\n",
      "config.status: creating src/Makefile\n",
      "config.status: creating src/Makefile.msvc\n",
      "config.status: creating man/Makefile\n",
      "config.status: creating doc/Makefile\n",
      "config.status: creating tests/Makefile\n",
      "config.status: creating swig/version.h\n",
      "config.status: creating mecab.iss\n",
      "config.status: creating mecab-config\n",
      "config.status: creating mecabrc\n",
      "config.status: creating config.h\n",
      "config.status: executing depfiles commands\n",
      "config.status: executing libtool commands\n",
      "config.status: executing default commands\n",
      "make  all-recursive\n",
      "make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2'\n",
      "Making all in src\n",
      "make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/src'\n",
      "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o viterbi.lo viterbi.cpp\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c viterbi.cpp  -fPIC -DPIC -o .libs/viterbi.o\n",
      "In file included from \u001b[01m\u001b[Kviterbi.cpp:14:0\u001b[m\u001b[K:\n",
      "\u001b[01m\u001b[Kparam.h:30:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[KTarget {anonymous}::lexical_cast(Source) [with Target = std::__cxx11::basic_string<char>; Source = std::__cxx11::basic_string<char>]\u001b[m\u001b[K’ defined but not used [\u001b[01;35m\u001b[K-Wunused-function\u001b[m\u001b[K]\n",
      " std::string \u001b[01;35m\u001b[Klexical_cast<std::string, std::string>\u001b[m\u001b[K(std::string arg) {\n",
      "             \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c viterbi.cpp -o viterbi.o >/dev/null 2>&1\n",
      "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o tagger.lo tagger.cpp\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c tagger.cpp  -fPIC -DPIC -o .libs/tagger.o\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c tagger.cpp -o tagger.o >/dev/null 2>&1\n",
      "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o utils.lo utils.cpp\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c utils.cpp  -fPIC -DPIC -o .libs/utils.o\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c utils.cpp -o utils.o >/dev/null 2>&1\n",
      "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o eval.lo eval.cpp\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c eval.cpp  -fPIC -DPIC -o .libs/eval.o\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c eval.cpp -o eval.o >/dev/null 2>&1\n",
      "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o iconv_utils.lo iconv_utils.cpp\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c iconv_utils.cpp  -fPIC -DPIC -o .libs/iconv_utils.o\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c iconv_utils.cpp -o iconv_utils.o >/dev/null 2>&1\n",
      "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o dictionary_rewriter.lo dictionary_rewriter.cpp\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c dictionary_rewriter.cpp  -fPIC -DPIC -o .libs/dictionary_rewriter.o\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c dictionary_rewriter.cpp -o dictionary_rewriter.o >/dev/null 2>&1\n",
      "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o dictionary_generator.lo dictionary_generator.cpp\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c dictionary_generator.cpp  -fPIC -DPIC -o .libs/dictionary_generator.o\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c dictionary_generator.cpp -o dictionary_generator.o >/dev/null 2>&1\n",
      "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o dictionary_compiler.lo dictionary_compiler.cpp\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c dictionary_compiler.cpp  -fPIC -DPIC -o .libs/dictionary_compiler.o\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c dictionary_compiler.cpp -o dictionary_compiler.o >/dev/null 2>&1\n",
      "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o context_id.lo context_id.cpp\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c context_id.cpp  -fPIC -DPIC -o .libs/context_id.o\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c context_id.cpp -o context_id.o >/dev/null 2>&1\n",
      "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o connector.lo connector.cpp\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c connector.cpp  -fPIC -DPIC -o .libs/connector.o\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c connector.cpp -o connector.o >/dev/null 2>&1\n",
      "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o nbest_generator.lo nbest_generator.cpp\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c nbest_generator.cpp  -fPIC -DPIC -o .libs/nbest_generator.o\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c nbest_generator.cpp -o nbest_generator.o >/dev/null 2>&1\n",
      "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o writer.lo writer.cpp\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c writer.cpp  -fPIC -DPIC -o .libs/writer.o\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c writer.cpp -o writer.o >/dev/null 2>&1\n",
      "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o string_buffer.lo string_buffer.cpp\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c string_buffer.cpp  -fPIC -DPIC -o .libs/string_buffer.o\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c string_buffer.cpp -o string_buffer.o >/dev/null 2>&1\n",
      "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o param.lo param.cpp\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c param.cpp  -fPIC -DPIC -o .libs/param.o\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c param.cpp -o param.o >/dev/null 2>&1\n",
      "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o tokenizer.lo tokenizer.cpp\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c tokenizer.cpp  -fPIC -DPIC -o .libs/tokenizer.o\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c tokenizer.cpp -o tokenizer.o >/dev/null 2>&1\n",
      "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o char_property.lo char_property.cpp\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c char_property.cpp  -fPIC -DPIC -o .libs/char_property.o\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c char_property.cpp -o char_property.o >/dev/null 2>&1\n",
      "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o dictionary.lo dictionary.cpp\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c dictionary.cpp  -fPIC -DPIC -o .libs/dictionary.o\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c dictionary.cpp -o dictionary.o >/dev/null 2>&1\n",
      "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o feature_index.lo feature_index.cpp\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c feature_index.cpp  -fPIC -DPIC -o .libs/feature_index.o\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c feature_index.cpp -o feature_index.o >/dev/null 2>&1\n",
      "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o lbfgs.lo lbfgs.cpp\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c lbfgs.cpp  -fPIC -DPIC -o .libs/lbfgs.o\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c lbfgs.cpp -o lbfgs.o >/dev/null 2>&1\n",
      "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o learner_tagger.lo learner_tagger.cpp\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c learner_tagger.cpp  -fPIC -DPIC -o .libs/learner_tagger.o\n",
      "\u001b[01m\u001b[Klearner_tagger.cpp:25:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kchar* MeCab::{anonymous}::mystrdup(const string&)\u001b[m\u001b[K’ defined but not used [\u001b[01;35m\u001b[K-Wunused-function\u001b[m\u001b[K]\n",
      " char *\u001b[01;35m\u001b[Kmystrdup\u001b[m\u001b[K(const std::string &str) {\n",
      "       \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c learner_tagger.cpp -o learner_tagger.o >/dev/null 2>&1\n",
      "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o learner.lo learner.cpp\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c learner.cpp  -fPIC -DPIC -o .libs/learner.o\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c learner.cpp -o learner.o >/dev/null 2>&1\n",
      "/bin/bash ../libtool --tag=CXX   --mode=compile g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o libmecab.lo libmecab.cpp\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c libmecab.cpp  -fPIC -DPIC -o .libs/libmecab.o\n",
      "libtool: compile:  g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102 -DMECAB_DEFAULT_RC=\\\"/usr/local/etc/mecabrc\\\" -O3 -Wall -c libmecab.cpp -o libmecab.o >/dev/null 2>&1\n",
      "/bin/bash ../libtool --tag=CXX   --mode=link g++  -O3 -Wall  -no-undefined -version-info 2:0:0  -o libmecab.la -rpath /usr/local/lib viterbi.lo tagger.lo utils.lo eval.lo iconv_utils.lo dictionary_rewriter.lo dictionary_generator.lo dictionary_compiler.lo context_id.lo connector.lo nbest_generator.lo writer.lo string_buffer.lo param.lo tokenizer.lo char_property.lo dictionary.lo feature_index.lo lbfgs.lo learner_tagger.lo learner.lo libmecab.lo  -lpthread -lpthread  -lstdc++ \n",
      "libtool: link: g++  -fPIC -DPIC -shared -nostdlib /usr/lib/gcc/x86_64-linux-gnu/7/../../../x86_64-linux-gnu/crti.o /usr/lib/gcc/x86_64-linux-gnu/7/crtbeginS.o  .libs/viterbi.o .libs/tagger.o .libs/utils.o .libs/eval.o .libs/iconv_utils.o .libs/dictionary_rewriter.o .libs/dictionary_generator.o .libs/dictionary_compiler.o .libs/context_id.o .libs/connector.o .libs/nbest_generator.o .libs/writer.o .libs/string_buffer.o .libs/param.o .libs/tokenizer.o .libs/char_property.o .libs/dictionary.o .libs/feature_index.o .libs/lbfgs.o .libs/learner_tagger.o .libs/learner.o .libs/libmecab.o   -lpthread -L/usr/lib/gcc/x86_64-linux-gnu/7 -L/usr/lib/gcc/x86_64-linux-gnu/7/../../../x86_64-linux-gnu -L/usr/lib/gcc/x86_64-linux-gnu/7/../../../../lib -L/lib/x86_64-linux-gnu -L/lib/../lib -L/usr/lib/x86_64-linux-gnu -L/usr/lib/../lib -L/usr/lib/gcc/x86_64-linux-gnu/7/../../.. -lstdc++ -lm -lc -lgcc_s /usr/lib/gcc/x86_64-linux-gnu/7/crtendS.o /usr/lib/gcc/x86_64-linux-gnu/7/../../../x86_64-linux-gnu/crtn.o  -O3   -Wl,-soname -Wl,libmecab.so.2 -o .libs/libmecab.so.2.0.0\n",
      "libtool: link: (cd \".libs\" && rm -f \"libmecab.so.2\" && ln -s \"libmecab.so.2.0.0\" \"libmecab.so.2\")\n",
      "libtool: link: (cd \".libs\" && rm -f \"libmecab.so\" && ln -s \"libmecab.so.2.0.0\" \"libmecab.so\")\n",
      "libtool: link: ar cru .libs/libmecab.a  viterbi.o tagger.o utils.o eval.o iconv_utils.o dictionary_rewriter.o dictionary_generator.o dictionary_compiler.o context_id.o connector.o nbest_generator.o writer.o string_buffer.o param.o tokenizer.o char_property.o dictionary.o feature_index.o lbfgs.o learner_tagger.o learner.o libmecab.o\n",
      "ar: `u' modifier ignored since `D' is the default (see `U')\n",
      "libtool: link: ranlib .libs/libmecab.a\n",
      "libtool: link: ( cd \".libs\" && rm -f \"libmecab.la\" && ln -s \"../libmecab.la\" \"libmecab.la\" )\n",
      "g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o mecab.o mecab.cpp\n",
      "/bin/bash ../libtool --tag=CXX   --mode=link g++  -O3 -Wall    -o mecab mecab.o libmecab.la -lpthread -lpthread  -lstdc++ \n",
      "libtool: link: g++ -O3 -Wall -o .libs/mecab mecab.o  ./.libs/libmecab.so -lpthread -lstdc++\n",
      "g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o mecab-dict-index.o mecab-dict-index.cpp\n",
      "/bin/bash ../libtool --tag=CXX   --mode=link g++  -O3 -Wall    -o mecab-dict-index mecab-dict-index.o libmecab.la -lpthread -lpthread  -lstdc++ \n",
      "libtool: link: g++ -O3 -Wall -o .libs/mecab-dict-index mecab-dict-index.o  ./.libs/libmecab.so -lpthread -lstdc++\n",
      "g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o mecab-dict-gen.o mecab-dict-gen.cpp\n",
      "/bin/bash ../libtool --tag=CXX   --mode=link g++  -O3 -Wall    -o mecab-dict-gen mecab-dict-gen.o libmecab.la -lpthread -lpthread  -lstdc++ \n",
      "libtool: link: g++ -O3 -Wall -o .libs/mecab-dict-gen mecab-dict-gen.o  ./.libs/libmecab.so -lpthread -lstdc++\n",
      "g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o mecab-cost-train.o mecab-cost-train.cpp\n",
      "/bin/bash ../libtool --tag=CXX   --mode=link g++  -O3 -Wall    -o mecab-cost-train mecab-cost-train.o libmecab.la -lpthread -lpthread  -lstdc++ \n",
      "libtool: link: g++ -O3 -Wall -o .libs/mecab-cost-train mecab-cost-train.o  ./.libs/libmecab.so -lpthread -lstdc++\n",
      "g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o mecab-system-eval.o mecab-system-eval.cpp\n",
      "/bin/bash ../libtool --tag=CXX   --mode=link g++  -O3 -Wall    -o mecab-system-eval mecab-system-eval.o libmecab.la -lpthread -lpthread  -lstdc++ \n",
      "libtool: link: g++ -O3 -Wall -o .libs/mecab-system-eval mecab-system-eval.o  ./.libs/libmecab.so -lpthread -lstdc++\n",
      "g++ -DHAVE_CONFIG_H -I. -I.. -DDIC_VERSION=102   -DMECAB_DEFAULT_RC=\"\\\"/usr/local/etc/mecabrc\\\"\"    -O3 -Wall  -c -o mecab-test-gen.o mecab-test-gen.cpp\n",
      "/bin/bash ../libtool --tag=CXX   --mode=link g++  -O3 -Wall    -o mecab-test-gen mecab-test-gen.o libmecab.la -lpthread -lpthread  -lstdc++ \n",
      "libtool: link: g++ -O3 -Wall -o .libs/mecab-test-gen mecab-test-gen.o  ./.libs/libmecab.so -lpthread -lstdc++\n",
      "make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/src'\n",
      "Making all in man\n",
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      "make[2]: Nothing to be done for 'all'.\n",
      "make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/man'\n",
      "Making all in doc\n",
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      "make[2]: Nothing to be done for 'all'.\n",
      "make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/doc'\n",
      "Making all in tests\n",
      "make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/tests'\n",
      "make[2]: Nothing to be done for 'all'.\n",
      "make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/tests'\n",
      "make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2'\n",
      "make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2'\n",
      "make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2'\n",
      "Making check in src\n",
      "make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/src'\n",
      "make[1]: Nothing to be done for 'check'.\n",
      "make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/src'\n",
      "Making check in man\n",
      "make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/man'\n",
      "make[1]: Nothing to be done for 'check'.\n",
      "make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/man'\n",
      "Making check in doc\n",
      "make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/doc'\n",
      "make[1]: Nothing to be done for 'check'.\n",
      "make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/doc'\n",
      "Making check in tests\n",
      "make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/tests'\n",
      "make  check-TESTS\n",
      "make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/tests'\n",
      "./pos-id.def is not found. minimum setting is used\n",
      "reading ./unk.def ... 2\n",
      "emitting double-array: 100% |###########################################| \n",
      "./model.def is not found. skipped.\n",
      "./pos-id.def is not found. minimum setting is used\n",
      "reading ./dic.csv ... 177\n",
      "emitting double-array: 100% |###########################################| \n",
      "reading ./matrix.def ... 178x178\n",
      "emitting matrix      : 100% |###########################################| \n",
      "\n",
      "done!\n",
      "./pos-id.def is not found. minimum setting is used\n",
      "reading ./unk.def ... 2\n",
      "emitting double-array: 100% |###########################################| \n",
      "./model.def is not found. skipped.\n",
      "./pos-id.def is not found. minimum setting is used\n",
      "reading ./dic.csv ... 83\n",
      "emitting double-array: 100% |###########################################| \n",
      "reading ./matrix.def ... 84x84\n",
      "emitting matrix      : 100% |###########################################| \n",
      "\n",
      "done!\n",
      "./pos-id.def is not found. minimum setting is used\n",
      "reading ./unk.def ... 2\n",
      "emitting double-array: 100% |###########################################| \n",
      "./model.def is not found. skipped.\n",
      "./pos-id.def is not found. minimum setting is used\n",
      "reading ./dic.csv ... 450\n",
      "emitting double-array: 100% |###########################################| \n",
      "reading ./matrix.def ... 1x1\n",
      "\n",
      "done!\n",
      "./pos-id.def is not found. minimum setting is used\n",
      "reading ./unk.def ... 2\n",
      "emitting double-array: 100% |###########################################| \n",
      "./model.def is not found. skipped.\n",
      "./pos-id.def is not found. minimum setting is used\n",
      "reading ./dic.csv ... 162\n",
      "emitting double-array: 100% |###########################################| \n",
      "reading ./matrix.def ... 3x3\n",
      "emitting matrix      : 100% |###########################################| \n",
      "\n",
      "done!\n",
      "./pos-id.def is not found. minimum setting is used\n",
      "reading ./unk.def ... 2\n",
      "emitting double-array: 100% |###########################################| \n",
      "./model.def is not found. skipped.\n",
      "./pos-id.def is not found. minimum setting is used\n",
      "reading ./dic.csv ... 4\n",
      "emitting double-array: 100% |###########################################| \n",
      "reading ./matrix.def ... 1x1\n",
      "\n",
      "done!\n",
      "./pos-id.def is not found. minimum setting is used\n",
      "reading ./unk.def ... 11\n",
      "emitting double-array: 100% |###########################################| \n",
      "./model.def is not found. skipped.\n",
      "./pos-id.def is not found. minimum setting is used\n",
      "reading ./dic.csv ... 1\n",
      "reading ./matrix.def ... 1x1\n",
      "\n",
      "done!\n",
      "./pos-id.def is not found. minimum setting is used\n",
      "reading ./unk.def ... 2\n",
      "emitting double-array: 100% |###########################################| \n",
      "./model.def is not found. skipped.\n",
      "./pos-id.def is not found. minimum setting is used\n",
      "reading ./dic.csv ... 1\n",
      "reading ./matrix.def ... 1x1\n",
      "\n",
      "done!\n",
      "PASS: run-dics.sh\n",
      "PASS: run-eval.sh\n",
      "seed/pos-id.def is not found. minimum setting is used\n",
      "reading seed/unk.def ... 40\n",
      "emitting double-array: 100% |###########################################| \n",
      "seed/model.def is not found. skipped.\n",
      "seed/pos-id.def is not found. minimum setting is used\n",
      "reading seed/dic.csv ... 4335\n",
      "emitting double-array: 100% |###########################################| \n",
      "reading seed/matrix.def ... 1x1\n",
      "\n",
      "done!\n",
      "reading corpus ...\n",
      "Number of sentences: 34\n",
      "Number of features:  64108\n",
      "eta:                 0.00005\n",
      "freq:                1\n",
      "eval-size:           6\n",
      "unk-eval-size:       4\n",
      "threads:             1\n",
      "charset:             EUC-JP\n",
      "C(sigma^2):          1.00000\n",
      "\n",
      "iter=0 err=1.00000 F=0.35771 target=2406.28355 diff=1.00000\n",
      "iter=1 err=0.97059 F=0.65652 target=1484.25231 diff=0.38318\n",
      "iter=2 err=0.91176 F=0.79331 target=863.32765 diff=0.41834\n",
      "iter=3 err=0.85294 F=0.89213 target=596.72480 diff=0.30881\n",
      "iter=4 err=0.61765 F=0.95467 target=336.30744 diff=0.43641\n",
      "iter=5 err=0.50000 F=0.96702 target=246.53039 diff=0.26695\n",
      "iter=6 err=0.35294 F=0.95472 target=188.93963 diff=0.23361\n",
      "iter=7 err=0.20588 F=0.99106 target=168.62665 diff=0.10751\n",
      "iter=8 err=0.05882 F=0.99777 target=158.64865 diff=0.05917\n",
      "iter=9 err=0.08824 F=0.99665 target=154.14530 diff=0.02839\n",
      "iter=10 err=0.08824 F=0.99665 target=151.94257 diff=0.01429\n",
      "iter=11 err=0.02941 F=0.99888 target=147.20825 diff=0.03116\n",
      "iter=12 err=0.00000 F=1.00000 target=147.34956 diff=0.00096\n",
      "iter=13 err=0.02941 F=0.99888 target=146.32592 diff=0.00695\n",
      "iter=14 err=0.00000 F=1.00000 target=145.77299 diff=0.00378\n",
      "iter=15 err=0.02941 F=0.99888 target=145.24641 diff=0.00361\n",
      "iter=16 err=0.00000 F=1.00000 target=144.96490 diff=0.00194\n",
      "iter=17 err=0.02941 F=0.99888 target=144.90246 diff=0.00043\n",
      "iter=18 err=0.00000 F=1.00000 target=144.75959 diff=0.00099\n",
      "iter=19 err=0.00000 F=1.00000 target=144.71727 diff=0.00029\n",
      "iter=20 err=0.00000 F=1.00000 target=144.66337 diff=0.00037\n",
      "iter=21 err=0.00000 F=1.00000 target=144.61349 diff=0.00034\n",
      "iter=22 err=0.00000 F=1.00000 target=144.62987 diff=0.00011\n",
      "iter=23 err=0.00000 F=1.00000 target=144.60060 diff=0.00020\n",
      "iter=24 err=0.00000 F=1.00000 target=144.59125 diff=0.00006\n",
      "iter=25 err=0.00000 F=1.00000 target=144.58619 diff=0.00004\n",
      "iter=26 err=0.00000 F=1.00000 target=144.58219 diff=0.00003\n",
      "iter=27 err=0.00000 F=1.00000 target=144.58059 diff=0.00001\n",
      "\n",
      "Done! writing model file ... \n",
      "model-ipadic.c1.0.f1.model is not a binary model. reopen it as text mode...\n",
      "reading seed/unk.def ... 40\n",
      "reading seed/dic.csv ... 4335\n",
      "emitting model-ipadic.c1.0.f1.dic/left-id.def/ model-ipadic.c1.0.f1.dic/right-id.def\n",
      "emitting model-ipadic.c1.0.f1.dic/unk.def ... 40\n",
      "emitting model-ipadic.c1.0.f1.dic/dic.csv ... 4335\n",
      "emitting matrix      : 100% |###########################################| \n",
      "copying seed/char.def to model-ipadic.c1.0.f1.dic/char.def\n",
      "copying seed/rewrite.def to model-ipadic.c1.0.f1.dic/rewrite.def\n",
      "copying seed/dicrc to model-ipadic.c1.0.f1.dic/dicrc\n",
      "copying seed/feature.def to model-ipadic.c1.0.f1.dic/feature.def\n",
      "copying model-ipadic.c1.0.f1.model to model-ipadic.c1.0.f1.dic/model.def\n",
      "\n",
      "done!\n",
      "model-ipadic.c1.0.f1.dic/pos-id.def is not found. minimum setting is used\n",
      "reading model-ipadic.c1.0.f1.dic/unk.def ... 40\n",
      "emitting double-array: 100% |###########################################| \n",
      "model-ipadic.c1.0.f1.dic/pos-id.def is not found. minimum setting is used\n",
      "reading model-ipadic.c1.0.f1.dic/dic.csv ... 4335\n",
      "emitting double-array: 100% |###########################################| \n",
      "reading model-ipadic.c1.0.f1.dic/matrix.def ... 346x346\n",
      "emitting matrix      : 100% |###########################################| \n",
      "\n",
      "done!\n",
      "              precision          recall         F\n",
      "LEVEL 0:    12.8959(57/442) 11.8998(57/479) 12.3779\n",
      "LEVEL 1:    12.2172(54/442) 11.2735(54/479) 11.7264\n",
      "LEVEL 2:    11.7647(52/442) 10.8559(52/479) 11.2921\n",
      "LEVEL 4:    11.7647(52/442) 10.8559(52/479) 11.2921\n",
      "PASS: run-cost-train.sh\n",
      "==================\n",
      "All 3 tests passed\n",
      "==================\n",
      "make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/tests'\n",
      "make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/tests'\n",
      "make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2'\n",
      "make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2'\n",
      "Making install in src\n",
      "make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/src'\n",
      "make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/src'\n",
      "test -z \"/usr/local/lib\" || /bin/mkdir -p \"/usr/local/lib\"\n",
      " /bin/bash ../libtool   --mode=install /usr/bin/install -c   libmecab.la '/usr/local/lib'\n",
      "libtool: install: /usr/bin/install -c .libs/libmecab.so.2.0.0 /usr/local/lib/libmecab.so.2.0.0\n",
      "libtool: install: (cd /usr/local/lib && { ln -s -f libmecab.so.2.0.0 libmecab.so.2 || { rm -f libmecab.so.2 && ln -s libmecab.so.2.0.0 libmecab.so.2; }; })\n",
      "libtool: install: (cd /usr/local/lib && { ln -s -f libmecab.so.2.0.0 libmecab.so || { rm -f libmecab.so && ln -s libmecab.so.2.0.0 libmecab.so; }; })\n",
      "libtool: install: /usr/bin/install -c .libs/libmecab.lai /usr/local/lib/libmecab.la\n",
      "libtool: install: /usr/bin/install -c .libs/libmecab.a /usr/local/lib/libmecab.a\n",
      "libtool: install: chmod 644 /usr/local/lib/libmecab.a\n",
      "libtool: install: ranlib /usr/local/lib/libmecab.a\n",
      "libtool: finish: PATH=\"/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/snap/bin:/sbin\" ldconfig -n /usr/local/lib\n",
      "----------------------------------------------------------------------\n",
      "Libraries have been installed in:\n",
      "   /usr/local/lib\n",
      "\n",
      "If you ever happen to want to link against installed libraries\n",
      "in a given directory, LIBDIR, you must either use libtool, and\n",
      "specify the full pathname of the library, or use the `-LLIBDIR'\n",
      "flag during linking and do at least one of the following:\n",
      "   - add LIBDIR to the `LD_LIBRARY_PATH' environment variable\n",
      "     during execution\n",
      "   - add LIBDIR to the `LD_RUN_PATH' environment variable\n",
      "     during linking\n",
      "   - use the `-Wl,-rpath -Wl,LIBDIR' linker flag\n",
      "   - have your system administrator add LIBDIR to `/etc/ld.so.conf'\n",
      "\n",
      "See any operating system documentation about shared libraries for\n",
      "more information, such as the ld(1) and ld.so(8) manual pages.\n",
      "----------------------------------------------------------------------\n",
      "test -z \"/usr/local/bin\" || /bin/mkdir -p \"/usr/local/bin\"\n",
      "  /bin/bash ../libtool   --mode=install /usr/bin/install -c mecab '/usr/local/bin'\n",
      "libtool: install: /usr/bin/install -c .libs/mecab /usr/local/bin/mecab\n",
      "test -z \"/usr/local/libexec/mecab\" || /bin/mkdir -p \"/usr/local/libexec/mecab\"\n",
      "  /bin/bash ../libtool   --mode=install /usr/bin/install -c mecab-dict-index mecab-dict-gen mecab-cost-train mecab-system-eval mecab-test-gen '/usr/local/libexec/mecab'\n",
      "libtool: install: /usr/bin/install -c .libs/mecab-dict-index /usr/local/libexec/mecab/mecab-dict-index\n",
      "libtool: install: /usr/bin/install -c .libs/mecab-dict-gen /usr/local/libexec/mecab/mecab-dict-gen\n",
      "libtool: install: /usr/bin/install -c .libs/mecab-cost-train /usr/local/libexec/mecab/mecab-cost-train\n",
      "libtool: install: /usr/bin/install -c .libs/mecab-system-eval /usr/local/libexec/mecab/mecab-system-eval\n",
      "libtool: install: /usr/bin/install -c .libs/mecab-test-gen /usr/local/libexec/mecab/mecab-test-gen\n",
      "test -z \"/usr/local/include\" || /bin/mkdir -p \"/usr/local/include\"\n",
      " /usr/bin/install -c -m 644 mecab.h '/usr/local/include'\n",
      "make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/src'\n",
      "make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/src'\n",
      "Making install in man\n",
      "make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/man'\n",
      "make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/man'\n",
      "make[2]: Nothing to be done for 'install-exec-am'.\n",
      "test -z \"/usr/local/share/man/man1\" || /bin/mkdir -p \"/usr/local/share/man/man1\"\n",
      " /usr/bin/install -c -m 644 mecab.1 '/usr/local/share/man/man1'\n",
      "make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/man'\n",
      "make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/man'\n",
      "Making install in doc\n",
      "make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/doc'\n",
      "make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/doc'\n",
      "make[2]: Nothing to be done for 'install-exec-am'.\n",
      "make[2]: Nothing to be done for 'install-data-am'.\n",
      "make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/doc'\n",
      "make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/doc'\n",
      "Making install in tests\n",
      "make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/tests'\n",
      "make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2/tests'\n",
      "make[2]: Nothing to be done for 'install-exec-am'.\n",
      "make[2]: Nothing to be done for 'install-data-am'.\n",
      "make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/tests'\n",
      "make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2/tests'\n",
      "make[1]: Entering directory '/tmp/mecab-0.996-ko-0.9.2'\n",
      "make[2]: Entering directory '/tmp/mecab-0.996-ko-0.9.2'\n",
      "test -z \"/usr/local/bin\" || /bin/mkdir -p \"/usr/local/bin\"\n",
      " /usr/bin/install -c mecab-config '/usr/local/bin'\n",
      "test -z \"/usr/local/etc\" || /bin/mkdir -p \"/usr/local/etc\"\n",
      " /usr/bin/install -c -m 644 mecabrc '/usr/local/etc'\n",
      "make[2]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2'\n",
      "make[1]: Leaving directory '/tmp/mecab-0.996-ko-0.9.2'\n",
      "Install mecab-ko-dic\n",
      "Install mecab-ko-dic\n",
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
      "100 47.4M  100 47.4M    0     0  17.0M      0  0:00:02  0:00:02 --:--:-- 27.5M\n",
      "mecab-ko-dic-2.1.1-20180720/\n",
      "mecab-ko-dic-2.1.1-20180720/configure\n",
      "mecab-ko-dic-2.1.1-20180720/COPYING\n",
      "mecab-ko-dic-2.1.1-20180720/autogen.sh\n",
      "mecab-ko-dic-2.1.1-20180720/Place-station.csv\n",
      "mecab-ko-dic-2.1.1-20180720/NNG.csv\n",
      "mecab-ko-dic-2.1.1-20180720/README\n",
      "mecab-ko-dic-2.1.1-20180720/EF.csv\n",
      "mecab-ko-dic-2.1.1-20180720/MAG.csv\n",
      "mecab-ko-dic-2.1.1-20180720/Preanalysis.csv\n",
      "mecab-ko-dic-2.1.1-20180720/NNB.csv\n",
      "mecab-ko-dic-2.1.1-20180720/Person-actor.csv\n",
      "mecab-ko-dic-2.1.1-20180720/VV.csv\n",
      "mecab-ko-dic-2.1.1-20180720/Makefile.in\n",
      "mecab-ko-dic-2.1.1-20180720/matrix.def\n",
      "mecab-ko-dic-2.1.1-20180720/EC.csv\n",
      "mecab-ko-dic-2.1.1-20180720/NNBC.csv\n",
      "mecab-ko-dic-2.1.1-20180720/clean\n",
      "mecab-ko-dic-2.1.1-20180720/ChangeLog\n",
      "mecab-ko-dic-2.1.1-20180720/J.csv\n",
      "mecab-ko-dic-2.1.1-20180720/.keep\n",
      "mecab-ko-dic-2.1.1-20180720/feature.def\n",
      "mecab-ko-dic-2.1.1-20180720/Foreign.csv\n",
      "mecab-ko-dic-2.1.1-20180720/XPN.csv\n",
      "mecab-ko-dic-2.1.1-20180720/EP.csv\n",
      "mecab-ko-dic-2.1.1-20180720/NR.csv\n",
      "mecab-ko-dic-2.1.1-20180720/left-id.def\n",
      "mecab-ko-dic-2.1.1-20180720/Place.csv\n",
      "mecab-ko-dic-2.1.1-20180720/Symbol.csv\n",
      "mecab-ko-dic-2.1.1-20180720/dicrc\n",
      "mecab-ko-dic-2.1.1-20180720/NP.csv\n",
      "mecab-ko-dic-2.1.1-20180720/ETM.csv\n",
      "mecab-ko-dic-2.1.1-20180720/IC.csv\n",
      "mecab-ko-dic-2.1.1-20180720/Place-address.csv\n",
      "mecab-ko-dic-2.1.1-20180720/Group.csv\n",
      "mecab-ko-dic-2.1.1-20180720/model.def\n",
      "mecab-ko-dic-2.1.1-20180720/XSN.csv\n",
      "mecab-ko-dic-2.1.1-20180720/INSTALL\n",
      "mecab-ko-dic-2.1.1-20180720/rewrite.def\n",
      "mecab-ko-dic-2.1.1-20180720/Inflect.csv\n",
      "mecab-ko-dic-2.1.1-20180720/configure.ac\n",
      "mecab-ko-dic-2.1.1-20180720/NNP.csv\n",
      "mecab-ko-dic-2.1.1-20180720/CoinedWord.csv\n",
      "mecab-ko-dic-2.1.1-20180720/XSV.csv\n",
      "mecab-ko-dic-2.1.1-20180720/pos-id.def\n",
      "mecab-ko-dic-2.1.1-20180720/Makefile.am\n",
      "mecab-ko-dic-2.1.1-20180720/unk.def\n",
      "mecab-ko-dic-2.1.1-20180720/missing\n",
      "mecab-ko-dic-2.1.1-20180720/VCP.csv\n",
      "mecab-ko-dic-2.1.1-20180720/install-sh\n",
      "mecab-ko-dic-2.1.1-20180720/Hanja.csv\n",
      "mecab-ko-dic-2.1.1-20180720/MAJ.csv\n",
      "mecab-ko-dic-2.1.1-20180720/XSA.csv\n",
      "mecab-ko-dic-2.1.1-20180720/Wikipedia.csv\n",
      "mecab-ko-dic-2.1.1-20180720/tools/\n",
      "mecab-ko-dic-2.1.1-20180720/tools/add-userdic.sh\n",
      "mecab-ko-dic-2.1.1-20180720/tools/mecab-bestn.sh\n",
      "mecab-ko-dic-2.1.1-20180720/tools/convert_for_using_store.sh\n",
      "mecab-ko-dic-2.1.1-20180720/user-dic/\n",
      "mecab-ko-dic-2.1.1-20180720/user-dic/nnp.csv\n",
      "mecab-ko-dic-2.1.1-20180720/user-dic/place.csv\n",
      "mecab-ko-dic-2.1.1-20180720/user-dic/person.csv\n",
      "mecab-ko-dic-2.1.1-20180720/user-dic/README.md\n",
      "mecab-ko-dic-2.1.1-20180720/NorthKorea.csv\n",
      "mecab-ko-dic-2.1.1-20180720/VX.csv\n",
      "mecab-ko-dic-2.1.1-20180720/right-id.def\n",
      "mecab-ko-dic-2.1.1-20180720/VA.csv\n",
      "mecab-ko-dic-2.1.1-20180720/char.def\n",
      "mecab-ko-dic-2.1.1-20180720/NEWS\n",
      "mecab-ko-dic-2.1.1-20180720/MM.csv\n",
      "mecab-ko-dic-2.1.1-20180720/ETN.csv\n",
      "mecab-ko-dic-2.1.1-20180720/AUTHORS\n",
      "mecab-ko-dic-2.1.1-20180720/Person.csv\n",
      "mecab-ko-dic-2.1.1-20180720/XR.csv\n",
      "mecab-ko-dic-2.1.1-20180720/VCN.csv\n",
      "Looking in current directory for macros.\n",
      "configure.ac:2: warning: AM_INIT_AUTOMAKE: two- and three-arguments forms are deprecated.  For more info, see:\n",
      "configure.ac:2: http://www.gnu.org/software/automake/manual/automake.html#Modernize-AM_005fINIT_005fAUTOMAKE-invocation\n",
      "checking for a BSD-compatible install... /usr/bin/install -c\n",
      "checking whether build environment is sane... yes\n",
      "/tmp/mecab-ko-dic-2.1.1-20180720/missing: Unknown `--is-lightweight' option\n",
      "Try `/tmp/mecab-ko-dic-2.1.1-20180720/missing --help' for more information\n",
      "configure: WARNING: 'missing' script is too old or missing\n",
      "checking for a thread-safe mkdir -p... /bin/mkdir -p\n",
      "checking for gawk... no\n",
      "checking for mawk... mawk\n",
      "checking whether make sets $(MAKE)... yes\n",
      "checking whether make supports nested variables... yes\n",
      "checking for mecab-config... /usr/local/bin/mecab-config\n",
      "checking that generated files are newer than configure... done\n",
      "configure: creating ./config.status\n",
      "config.status: creating Makefile\n",
      "/usr/local/lib\n",
      "/usr/local/libexec/mecab/mecab-dict-index -d . -o . -f UTF-8 -t UTF-8\n",
      "reading ./unk.def ... 13\n",
      "emitting double-array: 100% |###########################################| \n",
      "reading ./XPN.csv ... 83\n",
      "reading ./MAG.csv ... 14242\n",
      "reading ./Group.csv ... 3176\n",
      "reading ./EP.csv ... 51\n",
      "reading ./NNB.csv ... 140\n",
      "reading ./EF.csv ... 1820\n",
      "reading ./VCN.csv ... 7\n",
      "reading ./XSA.csv ... 19\n",
      "reading ./VA.csv ... 2360\n",
      "reading ./VV.csv ... 7331\n",
      "reading ./ETM.csv ... 133\n",
      "reading ./J.csv ... 416\n",
      "reading ./ETN.csv ... 14\n",
      "reading ./NR.csv ... 482\n",
      "reading ./NNBC.csv ... 677\n",
      "reading ./Person.csv ... 196459\n",
      "reading ./Symbol.csv ... 16\n",
      "reading ./XR.csv ... 3637\n",
      "reading ./Wikipedia.csv ... 36762\n",
      "reading ./IC.csv ... 1305\n",
      "reading ./MM.csv ... 453\n",
      "reading ./VX.csv ... 125\n",
      "reading ./CoinedWord.csv ... 148\n",
      "reading ./NorthKorea.csv ... 3\n",
      "reading ./VCP.csv ... 9\n",
      "reading ./NNP.csv ... 2371\n",
      "reading ./Person-actor.csv ... 99230\n",
      "reading ./EC.csv ... 2547\n",
      "reading ./Place-station.csv ... 1145\n",
      "reading ./XSN.csv ... 124\n",
      "reading ./NP.csv ... 342\n",
      "reading ./Preanalysis.csv ... 5\n",
      "reading ./Hanja.csv ... 125750\n",
      "reading ./NNG.csv ... 208524\n",
      "reading ./XSV.csv ... 23\n",
      "reading ./MAJ.csv ... 240\n",
      "reading ./Place.csv ... 30303\n",
      "reading ./Place-address.csv ... 19301\n",
      "reading ./Foreign.csv ... 11690\n",
      "reading ./Inflect.csv ... 44820\n",
      "emitting double-array: 100% |###########################################| \n",
      "reading ./matrix.def ... 3822x2693\n",
      "emitting matrix      : 100% |###########################################| \n",
      "\n",
      "done!\n",
      "echo To enable dictionary, rewrite /usr/local/etc/mecabrc as \\\"dicdir = /usr/local/lib/mecab/dic/mecab-ko-dic\\\"\n",
      "To enable dictionary, rewrite /usr/local/etc/mecabrc as \"dicdir = /usr/local/lib/mecab/dic/mecab-ko-dic\"\n",
      "make[1]: Entering directory '/tmp/mecab-ko-dic-2.1.1-20180720'\n",
      "make[1]: Nothing to be done for 'install-exec-am'.\n",
      " /bin/mkdir -p '/usr/local/lib/mecab/dic/mecab-ko-dic'\n",
      " /usr/bin/install -c -m 644 model.bin matrix.bin char.bin sys.dic unk.dic left-id.def right-id.def rewrite.def pos-id.def dicrc '/usr/local/lib/mecab/dic/mecab-ko-dic'\n",
      "make[1]: Leaving directory '/tmp/mecab-ko-dic-2.1.1-20180720'\n",
      "Install mecab-python\n",
      "/tmp /tmp/mecab-ko-dic-2.1.1-20180720\n",
      "Cloning into 'mecab-python-0.996'...\n",
      "Unpacking objects: 100% (17/17), done.\n",
      "/tmp/mecab-ko-dic-2.1.1-20180720\n",
      "Processing /tmp/mecab-python-0.996\n",
      "Building wheels for collected packages: mecab-python\n",
      "  Building wheel for mecab-python (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for mecab-python: filename=mecab_python-0.996_ko_0.9.2-cp37-cp37m-linux_x86_64.whl size=155544 sha256=511019cad88f2bc3c154e8fea644c9d2e20a768abb79b093f1654014e71c6c15\n",
      "  Stored in directory: /aiffel/.cache/pip/wheels/40/7b/9f/2922869bef86c3354ae7034f7a3647c573ee1997c2dad0290a\n",
      "Successfully built mecab-python\n",
      "Installing collected packages: mecab-python\n",
      "Successfully installed mecab-python-0.996-ko-0.9.2\n",
      "\u001b[33mWARNING: You are using pip version 20.3.3; however, version 21.3.1 is available.\n",
      "You should consider upgrading via the '/opt/conda/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "!bash <(curl -s https://raw.githubusercontent.com/konlpy/konlpy/master/scripts/mecab.sh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "atmospheric-bookmark",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: konlpy in /opt/conda/lib/python3.7/site-packages (0.5.2)\n",
      "Requirement already satisfied: tweepy>=3.7.0 in /opt/conda/lib/python3.7/site-packages (from konlpy) (3.10.0)\n",
      "Requirement already satisfied: JPype1>=0.7.0 in /opt/conda/lib/python3.7/site-packages (from konlpy) (1.2.1)\n",
      "Requirement already satisfied: lxml>=4.1.0 in /opt/conda/lib/python3.7/site-packages (from konlpy) (4.6.2)\n",
      "Requirement already satisfied: beautifulsoup4==4.6.0 in /opt/conda/lib/python3.7/site-packages (from konlpy) (4.6.0)\n",
      "Requirement already satisfied: colorama in /opt/conda/lib/python3.7/site-packages (from konlpy) (0.4.4)\n",
      "Requirement already satisfied: numpy>=1.6 in /opt/conda/lib/python3.7/site-packages (from konlpy) (1.19.5)\n",
      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from JPype1>=0.7.0->konlpy) (3.7.4.3)\n",
      "Requirement already satisfied: six>=1.10.0 in /opt/conda/lib/python3.7/site-packages (from tweepy>=3.7.0->konlpy) (1.15.0)\n",
      "Requirement already satisfied: requests[socks]>=2.11.1 in /opt/conda/lib/python3.7/site-packages (from tweepy>=3.7.0->konlpy) (2.25.1)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /opt/conda/lib/python3.7/site-packages (from tweepy>=3.7.0->konlpy) (1.3.0)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /opt/conda/lib/python3.7/site-packages (from requests-oauthlib>=0.7.0->tweepy>=3.7.0->konlpy) (3.1.0)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests[socks]>=2.11.1->tweepy>=3.7.0->konlpy) (1.26.2)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests[socks]>=2.11.1->tweepy>=3.7.0->konlpy) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests[socks]>=2.11.1->tweepy>=3.7.0->konlpy) (2020.12.5)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests[socks]>=2.11.1->tweepy>=3.7.0->konlpy) (2.10)\n",
      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /opt/conda/lib/python3.7/site-packages (from requests[socks]>=2.11.1->tweepy>=3.7.0->konlpy) (1.7.1)\n",
      "\u001b[33mWARNING: You are using pip version 20.3.3; however, version 21.3.1 is available.\n",
      "You should consider upgrading via the '/opt/conda/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!python3 -m pip install konlpy       # Python 3.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "subtle-affiliation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mecab-ko is already installed\n",
      "mecab-ko-dic is already installed\n",
      "mecab-python is already installed\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "!bash <(curl -s https://raw.githubusercontent.com/konlpy/konlpy/master/scripts/mecab.sh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "collaborative-appraisal",
   "metadata": {},
   "outputs": [],
   "source": [
    "from konlpy.tag import Mecab\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "\n",
    "tokenizer = Mecab()\n",
    "stopwords = ['의','가','이','은','들','는','좀','잘','걍','과','도','를','으로','자','에','와','한','하다']\n",
    "\n",
    "def load_data(train_data, test_data, num_words=10000):\n",
    "    train_data.drop_duplicates(subset=['document'], inplace=True)\n",
    "    train_data = train_data.dropna(how = 'any') \n",
    "    test_data.drop_duplicates(subset=['document'], inplace=True)\n",
    "    test_data = test_data.dropna(how = 'any') \n",
    "    \n",
    "    X_train = []\n",
    "    for sentence in train_data['document']:\n",
    "        temp_X = tokenizer.morphs(sentence) # 토큰화\n",
    "        temp_X = [word for word in temp_X if not word in stopwords] # 불용어 제거\n",
    "        X_train.append(temp_X)\n",
    "\n",
    "    X_test = []\n",
    "    for sentence in test_data['document']:\n",
    "        temp_X = tokenizer.morphs(sentence) # 토큰화\n",
    "        temp_X = [word for word in temp_X if not word in stopwords] # 불용어 제거\n",
    "        X_test.append(temp_X)\n",
    "    \n",
    "    words = np.concatenate(X_train).tolist()\n",
    "    counter = Counter(words)\n",
    "    counter = counter.most_common(10000-4)\n",
    "    vocab = ['', '', '', ''] + [key for key, _ in counter]\n",
    "    word_to_index = {word:index for index, word in enumerate(vocab)}\n",
    "        \n",
    "    def wordlist_to_indexlist(wordlist):\n",
    "        return [word_to_index[word] if word in word_to_index else word_to_index[''] for word in wordlist]\n",
    "        \n",
    "    X_train = list(map(wordlist_to_indexlist, X_train))\n",
    "    X_test = list(map(wordlist_to_indexlist, X_test))\n",
    "        \n",
    "    return X_train, np.array(list(train_data['label'])), X_test, np.array(list(test_data['label'])), word_to_index\n",
    "    \n",
    "X_train, y_train, X_test, y_test, word_to_index = load_data(train_data, test_data) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "loving-hobby",
   "metadata": {},
   "source": [
    "> 사실 해당 함수를 나름 구현해보겠다고 해봤으나 `사전word_to_index구성`부터 막혀서 일단 아이펠의 코드를 복붙했다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "opened-cartoon",
   "metadata": {},
   "outputs": [],
   "source": [
    "# decode용 딕셔너리\n",
    "index_to_word = {index:word for word, index in word_to_index.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "extended-racing",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 문장 1개를 활용할 딕셔너리와 함께 주면, 단어 인덱스 리스트 벡터로 변환해 주는 함수입니다. \n",
    "# 단, 모든 문장은 <BOS>로 시작하는 것으로 합니다. \n",
    "def get_encoded_sentence(sentence, word_to_index):\n",
    "    return [word_to_index['<BOS>']]+[word_to_index[word] if word in word_to_index else word_to_index['<UNK>'] for word in sentence.split()]\n",
    "\n",
    "# 여러 개의 문장 리스트를 한꺼번에 단어 인덱스 리스트 벡터로 encode해 주는 함수입니다. \n",
    "def get_encoded_sentences(sentences, word_to_index):\n",
    "    return [get_encoded_sentence(sentence, word_to_index) for sentence in sentences]\n",
    "\n",
    "# 숫자 벡터로 encode된 문장을 원래대로 decode하는 함수입니다. \n",
    "def get_decoded_sentence(encoded_sentence, index_to_word):\n",
    "    return ' '.join(index_to_word[index] if index in index_to_word else '<UNK>' for index in encoded_sentence[1:])  #[1:]를 통해 <BOS>를 제외\n",
    "\n",
    "# 여러 개의 숫자 벡터로 encode된 문장을 한꺼번에 원래대로 decode하는 함수입니다. \n",
    "def get_decoded_sentences(encoded_sentences, index_to_word):\n",
    "    return [get_decoded_sentence(encoded_sentence, index_to_word) for encoded_sentence in encoded_sentences]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sticky-associate",
   "metadata": {},
   "source": [
    "## # 모델 구성을 위한 데이터 분석 및 가공\n",
    "> 문장의 평균 길이와 표준편차, 그리고 이 둘을 이용해 95%의 문장을 커버하는 max_len을 구해야 한다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "medical-separate",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    평균 : 15.981687211831826\n",
      " 표준편차 : 12.824686694216712\n",
      "max_len : 41\n",
      "커버 범위 : 93.049076%\n"
     ]
    }
   ],
   "source": [
    "sentence_lengths = np.array([len(indexed_sentence) for indexed_sentence in X_train])\n",
    "mean = sentence_lengths.mean()\n",
    "std = sentence_lengths.std()\n",
    "max_len = int(mean + 2 * std)\n",
    "\n",
    "print(f'    평균 : {mean}')\n",
    "print(f' 표준편차 : {std}')\n",
    "print(f'max_len : {max_len}')\n",
    "print(f'커버 범위 : {len(sentence_lengths[sentence_lengths < max_len]) / len(sentence_lengths):2%}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "linear-depression",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.kdeplot(sentence_lengths)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "rubber-billion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[32, 74, 919, 4, 4, 39, 228, 20, 33, 748],\n",
       " [1028, 4, 11, 519, 549, 687, 5, 120, 4, 70, 1639, 52, 925, 1006, 15, 44, 391],\n",
       " [24, 205, 3],\n",
       " [8410, 158, 3925, 4, 4, 298, 94, 17, 6, 4, 4, 58, 3479]]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mechanical-guyana",
   "metadata": {},
   "source": [
    "#### 패딩 추가하기"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "neutral-creek",
   "metadata": {},
   "source": [
    "> 패딩을 추가하려다 보니 `<BOS>`, `<PAD>`, `<UNK>` 가 word_to_index에 없어서 오류가 났다.  \n",
    "> word_to_index 맨 앞쪽에 추가하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "union-collect",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "False\n",
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "print('<BOS>' in word_to_index)\n",
    "print('<PAD>' in word_to_index)\n",
    "print('<UNK>' in word_to_index)\n",
    "print('.' in word_to_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "equipped-psychology",
   "metadata": {},
   "outputs": [],
   "source": [
    "word_to_index['<PAD>'] = 0\n",
    "word_to_index['<BOS>'] = 1\n",
    "word_to_index['<UNK>'] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "directed-sensitivity",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "print('<BOS>' in word_to_index)\n",
    "print('<PAD>' in word_to_index)\n",
    "print('<UNK>' in word_to_index)\n",
    "print('.' in word_to_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "olympic-compound",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(146182, 41)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "# 'pre'로 데이터에 패딩 추가\n",
    "X_train_pre = tf.keras.preprocessing.sequence.pad_sequences(X_train,\n",
    "                                                        value=word_to_index[\"<PAD>\"],\n",
    "                                                        padding='pre',\n",
    "                                                        maxlen=max_len)\n",
    "\n",
    "X_test_pre = tf.keras.preprocessing.sequence.pad_sequences(X_test,\n",
    "                                                       value=word_to_index[\"<PAD>\"],\n",
    "                                                       padding='pre',\n",
    "                                                       maxlen=max_len)\n",
    "\n",
    "print(X_train_pre.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "patent-access",
   "metadata": {},
   "source": [
    "## # 모델 구성 및 validation set 구성"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mature-schema",
   "metadata": {},
   "source": [
    "#### 모델은 아래의 것들로 구성해 볼 예정이다.\n",
    "- LSTM 이용\n",
    "- 1-D CNN 이용\n",
    "- Global Max Pooling 이용\n",
    "- 그리고 가능하다면 인터넷에서 찾아본 다른 모델 이용(원리는 아직 전혀 모른다...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "collective-ethnic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 먼저 `vocab_size` 다시 한 번 확인\n",
    "len(word_to_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prime-internet",
   "metadata": {},
   "source": [
    "> Embedding Layer의 `output_dim` 파라미터...  \n",
    "> '왜 4를 넣는거지?' 하고 Word2Vec에 관한 블로그를 읽어도 이해가 되지 않던 중 결국 같은 LMS 조원들의 도움 덕분에 저것이 임의로 지정하는 하이퍼파라미터라는 것을 알게 되었다.  \n",
    "> 그래서 나는 여기서 인터넷 검색하면서 발견한 '32'라는 숫자를 넣어보겠다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "martial-cosmetic",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding (Embedding)        (None, None, 32)          320000    \n",
      "_________________________________________________________________\n",
      "lstm (LSTM)                  (None, 64)                24832     \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 346,945\n",
      "Trainable params: 346,945\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# RNN 모델\n",
    "vocab_size = 10000\n",
    "word_vector_dim = 32 \n",
    "\n",
    "model_lstm = tf.keras.Sequential()\n",
    "model_lstm.add(tf.keras.layers.Embedding(vocab_size, word_vector_dim, input_shape=(None,)))\n",
    "model_lstm.add(tf.keras.layers.LSTM(64))\n",
    "model_lstm.add(tf.keras.layers.Dense(32, activation='relu'))\n",
    "model_lstm.add(tf.keras.layers.Dense(1, activation='sigmoid'))  # 최종 출력은 긍정/부정을 나타내는 1dim 입니다.\n",
    "\n",
    "model_lstm.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "aggregate-manufacturer",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, None, 32)          320000    \n",
      "_________________________________________________________________\n",
      "conv1d (Conv1D)              (None, None, 32)          7200      \n",
      "_________________________________________________________________\n",
      "max_pooling1d (MaxPooling1D) (None, None, 32)          0         \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, None, 32)          7200      \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d (Global (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 334,945\n",
      "Trainable params: 334,945\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 1-D CNN으로 만든 모델 구현\n",
    "\n",
    "model_1dcon = tf.keras.Sequential()\n",
    "model_1dcon.add(tf.keras.layers.Embedding(vocab_size, word_vector_dim, input_shape=(None,)))\n",
    "model_1dcon.add(tf.keras.layers.Conv1D(32, 7, activation='relu')) # word_vector_dim과 맞춰봤습니다.\n",
    "model_1dcon.add(tf.keras.layers.MaxPooling1D(5))\n",
    "model_1dcon.add(tf.keras.layers.Conv1D(32, 7, activation='relu')) # word_vector_dim과 맞춰봤습니다.\n",
    "model_1dcon.add(tf.keras.layers.GlobalMaxPooling1D())\n",
    "model_1dcon.add(tf.keras.layers.Dense(16, activation='relu'))\n",
    "model_1dcon.add(tf.keras.layers.Dense(1, activation='sigmoid'))  # 최종 출력은 긍정/부정을 나타내는 1dim 입니다.\n",
    "\n",
    "model_1dcon.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "round-struggle",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_2 (Embedding)      (None, None, 32)          320000    \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_1 (Glob (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 320,545\n",
      "Trainable params: 320,545\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# GlobalMaxPolling만 있는 것 \n",
    "\n",
    "model_gmp = tf.keras.Sequential()\n",
    "model_gmp.add(tf.keras.layers.Embedding(vocab_size, word_vector_dim, input_shape=(None,)))\n",
    "model_gmp.add(tf.keras.layers.GlobalMaxPooling1D())\n",
    "model_gmp.add(tf.keras.layers.Dense(16, activation='relu'))\n",
    "model_gmp.add(tf.keras.layers.Dense(1, activation='sigmoid'))  # 최종 출력은 긍정/부정을 나타내는 1dim 입니다.\n",
    "\n",
    "model_gmp.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "sufficient-cyprus",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_3 (Embedding)      (None, None, 32)          320000    \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, None, 1024)        33792     \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, None, 512)         524800    \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, None, 256)         131328    \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, None, 256)         65792     \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, None, 256)         65792     \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, None, 256)         65792     \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, None, 128)         32896     \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, None, 64)          8256      \n",
      "_________________________________________________________________\n",
      "dense_14 (Dense)             (None, None, 1)           65        \n",
      "=================================================================\n",
      "Total params: 1,248,513\n",
      "Trainable params: 1,248,513\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Dense Layer만으로 파라미터 많이 생성되게 한 모델\n",
    "# 파라미터가 많아지면 좋아지나 싶어서 100만 넘도록 해봄\n",
    "\n",
    "model_dense = tf.keras.Sequential()\n",
    "model_dense.add(tf.keras.layers.Embedding(vocab_size, word_vector_dim, input_shape=(None,)))\n",
    "model_dense.add(tf.keras.layers.Dense(1024, activation='relu'))\n",
    "model_dense.add(tf.keras.layers.Dense(512, activation='relu'))\n",
    "model_dense.add(tf.keras.layers.Dense(256, activation='relu'))\n",
    "model_dense.add(tf.keras.layers.Dense(256, activation='relu'))\n",
    "model_dense.add(tf.keras.layers.Dense(256, activation='relu'))\n",
    "model_dense.add(tf.keras.layers.Dense(256, activation='relu'))\n",
    "model_dense.add(tf.keras.layers.Dense(128, activation='relu'))\n",
    "model_dense.add(tf.keras.layers.Dense(64, activation='relu'))\n",
    "model_dense.add(tf.keras.layers.Dense(1, activation='sigmoid'))  # 최종 출력은 긍정/부정을 나타내는 1dim 입니다.\n",
    "\n",
    "model_dense.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ongoing-robertson",
   "metadata": {},
   "source": [
    "### Validation set 구분"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "wired-paintball",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_test_split으로 나눠보겠습니다.\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "xx_train, xx_validation, yy_train, yy_validation = train_test_split(X_train_pre,\n",
    "                                                                   y_train,\n",
    "                                                                   test_size=0.2,\n",
    "                                                                   random_state=2022)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "healthy-guarantee",
   "metadata": {},
   "source": [
    "### 모델 각각 훈련"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "manual-needle",
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "occupational-egypt",
   "metadata": {},
   "source": [
    "#### # LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "mexican-infrastructure",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "229/229 - 7s - loss: 0.4390 - accuracy: 0.7947 - val_loss: 0.3615 - val_accuracy: 0.8411\n",
      "Epoch 2/10\n",
      "229/229 - 3s - loss: 0.3335 - accuracy: 0.8571 - val_loss: 0.3567 - val_accuracy: 0.8461\n",
      "Epoch 3/10\n",
      "229/229 - 3s - loss: 0.3117 - accuracy: 0.8683 - val_loss: 0.3450 - val_accuracy: 0.8491\n",
      "Epoch 4/10\n",
      "229/229 - 3s - loss: 0.2958 - accuracy: 0.8742 - val_loss: 0.3497 - val_accuracy: 0.8481\n",
      "Epoch 5/10\n",
      "229/229 - 3s - loss: 0.2800 - accuracy: 0.8803 - val_loss: 0.3525 - val_accuracy: 0.8520\n",
      "Epoch 6/10\n",
      "229/229 - 3s - loss: 0.2630 - accuracy: 0.8873 - val_loss: 0.3567 - val_accuracy: 0.8496\n",
      "Epoch 7/10\n",
      "229/229 - 3s - loss: 0.2445 - accuracy: 0.8955 - val_loss: 0.3873 - val_accuracy: 0.8505\n",
      "Epoch 8/10\n",
      "229/229 - 3s - loss: 0.2268 - accuracy: 0.9039 - val_loss: 0.3935 - val_accuracy: 0.8501\n",
      "Epoch 9/10\n",
      "229/229 - 3s - loss: 0.2136 - accuracy: 0.9098 - val_loss: 0.4052 - val_accuracy: 0.8499\n",
      "Epoch 10/10\n",
      "229/229 - 3s - loss: 0.1964 - accuracy: 0.9175 - val_loss: 0.4283 - val_accuracy: 0.8418\n"
     ]
    }
   ],
   "source": [
    "model_lstm.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model_lstm.fit(xx_train,\n",
    "                         yy_train,\n",
    "                         epochs=EPOCHS,\n",
    "                         batch_size=512,\n",
    "                        validation_data=(xx_validation, yy_validation),\n",
    "                        verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "nasty-scratch",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1537/1537 - 5s - loss: 0.4241 - accuracy: 0.8403\n",
      "[0.42413806915283203, 0.8403482437133789]\n"
     ]
    }
   ],
   "source": [
    "# test 셋으로 평가해보기\n",
    "results = model_lstm.evaluate(X_test_pre, y_test, verbose=2)\n",
    "\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "basic-civilization",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 그래프로 그려보기\n",
    "\n",
    "acc = history.history['accuracy']\n",
    "loss = history.history['loss']\n",
    "val_acc = history.history['val_accuracy']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(acc, label='training')\n",
    "plt.plot(val_acc, 'o', label='validation')\n",
    "plt.xlabel('epocks')\n",
    "plt.ylabel('accuracy', fontsize=20)\n",
    "plt.legend(fontsize=20)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(loss, label='training')\n",
    "plt.plot(val_loss, 'o', label='validation')\n",
    "plt.xlabel('epocks')\n",
    "plt.ylabel('loss', fontsize=20)\n",
    "plt.legend(fontsize=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "worldwide-reception",
   "metadata": {},
   "source": [
    "> LSTM의 경우(에포크 10)\n",
    ">  - train = 약 92%, val = 약 84%, test = 약 84%가 나왔다.\n",
    ">  - 특히 에포크마다 같이 테스트된 validation 셋의 지표는 더 좋아지지 않았고 에포크 5부터는 overfitting이 일어나기 시작했다.  \n",
    ">  - 해당 레이어로 설계된 모델 혹은 임베딩된 워드벡터 품질이 안 좋은 것 같다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aerial-issue",
   "metadata": {},
   "source": [
    "#### # 1-D Con"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "technological-constant",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "229/229 - 9s - loss: 0.4564 - accuracy: 0.7795 - val_loss: 0.3497 - val_accuracy: 0.8481\n",
      "Epoch 2/10\n",
      "229/229 - 2s - loss: 0.3153 - accuracy: 0.8665 - val_loss: 0.3436 - val_accuracy: 0.8490\n",
      "Epoch 3/10\n",
      "229/229 - 2s - loss: 0.2736 - accuracy: 0.8873 - val_loss: 0.3375 - val_accuracy: 0.8542\n",
      "Epoch 4/10\n",
      "229/229 - 2s - loss: 0.2290 - accuracy: 0.9100 - val_loss: 0.3576 - val_accuracy: 0.8525\n",
      "Epoch 5/10\n",
      "229/229 - 2s - loss: 0.1791 - accuracy: 0.9334 - val_loss: 0.3945 - val_accuracy: 0.8471\n",
      "Epoch 6/10\n",
      "229/229 - 2s - loss: 0.1308 - accuracy: 0.9549 - val_loss: 0.4503 - val_accuracy: 0.8419\n",
      "Epoch 7/10\n",
      "229/229 - 2s - loss: 0.0938 - accuracy: 0.9699 - val_loss: 0.5108 - val_accuracy: 0.8384\n",
      "Epoch 8/10\n",
      "229/229 - 2s - loss: 0.0680 - accuracy: 0.9792 - val_loss: 0.5794 - val_accuracy: 0.8366\n",
      "Epoch 9/10\n",
      "229/229 - 2s - loss: 0.0503 - accuracy: 0.9855 - val_loss: 0.6538 - val_accuracy: 0.8322\n",
      "Epoch 10/10\n",
      "229/229 - 2s - loss: 0.0395 - accuracy: 0.9883 - val_loss: 0.7521 - val_accuracy: 0.8276\n"
     ]
    }
   ],
   "source": [
    "model_1dcon.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model_1dcon.fit(xx_train,\n",
    "                         yy_train,\n",
    "                         epochs=EPOCHS,\n",
    "                         batch_size=512,\n",
    "                        validation_data=(xx_validation, yy_validation),\n",
    "                        verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "smart-filter",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1537/1537 - 4s - loss: 0.7618 - accuracy: 0.8293\n",
      "[0.761829137802124, 0.8292816877365112]\n"
     ]
    }
   ],
   "source": [
    "# test 셋으로 평가해보기\n",
    "results = model_1dcon.evaluate(X_test_pre, y_test, verbose=2)\n",
    "\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "concerned-preview",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 그래프로 그려보기\n",
    "\n",
    "acc = history.history['accuracy']\n",
    "loss = history.history['loss']\n",
    "val_acc = history.history['val_accuracy']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(acc, label='training')\n",
    "plt.plot(val_acc, 'o', label='validation')\n",
    "plt.xlabel('epocks')\n",
    "plt.ylabel('accuracy', fontsize=20)\n",
    "plt.legend(fontsize=20)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(loss, label='training')\n",
    "plt.plot(val_loss, 'o', label='validation')\n",
    "plt.xlabel('epocks')\n",
    "plt.ylabel('loss', fontsize=20)\n",
    "plt.legend(fontsize=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "valuable-creek",
   "metadata": {},
   "source": [
    "> 1-D CNN의 경우(에포크 10)\n",
    ">  - train = 약 99%, val = 약 83%, test = 약 82%가 나왔다.\n",
    ">  - 위의 LSTM과 마찬가지로 트레인셋에만 훌륭하게 학습되었다.  \n",
    ">  - 다만 LSTM 모델보다는 빠르게(?) overfitting 되었다.\n",
    ">  - 해당 레이어로 설계된 모델 혹은 임베딩된 워드벡터 품질이 안 좋은 것 같다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "super-maintenance",
   "metadata": {},
   "source": [
    "#### # Global Max Pooling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "convertible-function",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "229/229 - 2s - loss: 0.5240 - accuracy: 0.7877 - val_loss: 0.3804 - val_accuracy: 0.8316\n",
      "Epoch 2/10\n",
      "229/229 - 1s - loss: 0.3388 - accuracy: 0.8540 - val_loss: 0.3519 - val_accuracy: 0.8447\n",
      "Epoch 3/10\n",
      "229/229 - 1s - loss: 0.2956 - accuracy: 0.8762 - val_loss: 0.3496 - val_accuracy: 0.8468\n",
      "Epoch 4/10\n",
      "229/229 - 1s - loss: 0.2658 - accuracy: 0.8917 - val_loss: 0.3561 - val_accuracy: 0.8479\n",
      "Epoch 5/10\n",
      "229/229 - 1s - loss: 0.2406 - accuracy: 0.9048 - val_loss: 0.3660 - val_accuracy: 0.8450\n",
      "Epoch 6/10\n",
      "229/229 - 1s - loss: 0.2180 - accuracy: 0.9154 - val_loss: 0.3833 - val_accuracy: 0.8443\n",
      "Epoch 7/10\n",
      "229/229 - 1s - loss: 0.1976 - accuracy: 0.9252 - val_loss: 0.4026 - val_accuracy: 0.8406\n",
      "Epoch 8/10\n",
      "229/229 - 1s - loss: 0.1786 - accuracy: 0.9342 - val_loss: 0.4248 - val_accuracy: 0.8384\n",
      "Epoch 9/10\n",
      "229/229 - 1s - loss: 0.1614 - accuracy: 0.9419 - val_loss: 0.4483 - val_accuracy: 0.8362\n",
      "Epoch 10/10\n",
      "229/229 - 1s - loss: 0.1457 - accuracy: 0.9485 - val_loss: 0.4742 - val_accuracy: 0.8337\n"
     ]
    }
   ],
   "source": [
    "model_gmp.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model_gmp.fit(xx_train,\n",
    "                         yy_train,\n",
    "                         epochs=EPOCHS,\n",
    "                         batch_size=512,\n",
    "                        validation_data=(xx_validation, yy_validation),\n",
    "                        verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "figured-republican",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1537/1537 - 2s - loss: 0.4766 - accuracy: 0.8339\n",
      "[0.47662121057510376, 0.833858847618103]\n"
     ]
    }
   ],
   "source": [
    "# test 셋으로 평가해보기\n",
    "results = model_gmp.evaluate(X_test_pre, y_test, verbose=2)\n",
    "\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "coordinate-transition",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 그래프로 그려보기\n",
    "\n",
    "acc = history.history['accuracy']\n",
    "loss = history.history['loss']\n",
    "val_acc = history.history['val_accuracy']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(acc, label='training')\n",
    "plt.plot(val_acc, 'o', label='validation')\n",
    "plt.xlabel('epocks')\n",
    "plt.ylabel('accuracy', fontsize=20)\n",
    "plt.legend(fontsize=20)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(loss, label='training')\n",
    "plt.plot(val_loss, 'o', label='validation')\n",
    "plt.xlabel('epocks')\n",
    "plt.ylabel('loss', fontsize=20)\n",
    "plt.legend(fontsize=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sporting-species",
   "metadata": {},
   "source": [
    "> GMP의 경우(에포크 10)\n",
    ">  - train = 약 94%, val = 약 83%, test = 약 83%가 나왔다.\n",
    ">  - 위의 두 모델과 마찬가지로 트레인셋에만 훌륭하게 학습되었다.  \n",
    ">  - 해당 레이어로 설계된 모델 혹은 임베딩된 워드벡터 품질이 안 좋은 것 같다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "confidential-mexico",
   "metadata": {},
   "source": [
    "#### # Dense layer로만 100만 파라미터 넘게"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "secret-banking",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "229/229 - 24s - loss: 0.6842 - accuracy: 0.5238 - val_loss: 0.6760 - val_accuracy: 0.5388\n",
      "Epoch 2/10\n",
      "229/229 - 22s - loss: 0.6738 - accuracy: 0.5424 - val_loss: 0.6759 - val_accuracy: 0.5430\n",
      "Epoch 3/10\n",
      "229/229 - 22s - loss: 0.6729 - accuracy: 0.5440 - val_loss: 0.6747 - val_accuracy: 0.5428\n",
      "Epoch 4/10\n",
      "229/229 - 22s - loss: 0.6726 - accuracy: 0.5448 - val_loss: 0.6747 - val_accuracy: 0.5428\n",
      "Epoch 5/10\n",
      "229/229 - 22s - loss: 0.6725 - accuracy: 0.5451 - val_loss: 0.6746 - val_accuracy: 0.5433\n",
      "Epoch 6/10\n",
      "229/229 - 22s - loss: 0.6724 - accuracy: 0.5456 - val_loss: 0.6759 - val_accuracy: 0.5434\n",
      "Epoch 7/10\n",
      "229/229 - 22s - loss: 0.6724 - accuracy: 0.5462 - val_loss: 0.6749 - val_accuracy: 0.5432\n",
      "Epoch 8/10\n",
      "229/229 - 22s - loss: 0.6724 - accuracy: 0.5455 - val_loss: 0.6750 - val_accuracy: 0.5418\n",
      "Epoch 9/10\n",
      "229/229 - 22s - loss: 0.6722 - accuracy: 0.5459 - val_loss: 0.6745 - val_accuracy: 0.5430\n",
      "Epoch 10/10\n",
      "229/229 - 22s - loss: 0.6722 - accuracy: 0.5453 - val_loss: 0.6745 - val_accuracy: 0.5431\n"
     ]
    }
   ],
   "source": [
    "model_dense.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model_dense.fit(xx_train,\n",
    "                         yy_train,\n",
    "                         epochs=EPOCHS,\n",
    "                         batch_size=512,\n",
    "                        validation_data=(xx_validation, yy_validation),\n",
    "                        verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dried-admission",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1537/1537 - 9s - loss: 0.6743 - accuracy: 0.5459\n",
      "[0.6742663383483887, 0.5459061861038208]\n"
     ]
    }
   ],
   "source": [
    "# test 셋으로 평가해보기\n",
    "results = model_dense.evaluate(X_test_pre, y_test, verbose=2)\n",
    "\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "multiple-accounting",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 그래프로 그려보기\n",
    "\n",
    "acc = history.history['accuracy']\n",
    "loss = history.history['loss']\n",
    "val_acc = history.history['val_accuracy']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(acc, label='training')\n",
    "plt.plot(val_acc, 'o', label='validation')\n",
    "plt.xlabel('epocks')\n",
    "plt.ylabel('accuracy', fontsize=20)\n",
    "plt.legend(fontsize=20)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(loss, label='training')\n",
    "plt.plot(val_loss, 'o', label='validation')\n",
    "plt.xlabel('epocks')\n",
    "plt.ylabel('loss', fontsize=20)\n",
    "plt.legend(fontsize=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "western-failing",
   "metadata": {},
   "source": [
    "> dense layer만 있는 경우(에포크 10)\n",
    ">  - train = 약 54%, val = 약 54%, test = 약 54%가 나왔다.\n",
    ">  - 둘 중 하나 찍어도 50%인데 이건 모델 자체도 별로인 듯하다....\n",
    ">  - Dense layer만 있으면 대략 아무 것도 못한다는 배움(?)을 얻은 것 같다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "thermal-microphone",
   "metadata": {},
   "source": [
    "## # 학습된 임베딩 레이어 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "controlling-missile",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 32)\n"
     ]
    }
   ],
   "source": [
    "embedding_layer = model_lstm.layers[0]\n",
    "weights = embedding_layer.get_weights()[0]\n",
    "print(weights.shape)    # shape: (vocab_size, embedding_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "healthy-investment",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# 학습한 Embedding 파라미터를 파일에 써서 저장 \n",
    "word2vec_file_path = os.getenv('HOME')+'/aiffel/sentiment_classification/data/practice_word2vec.txt'\n",
    "f = open(word2vec_file_path, 'w')\n",
    "f.write('{} {}\\n'.format(vocab_size-4, word_vector_dim))  # 몇개의 벡터를 얼마 사이즈로 기재할지 타이틀을 씁니다.\n",
    "\n",
    "# 단어 개수(에서 특수문자 4개는 제외하고)만큼의 워드 벡터를 파일에 기록합니다. \n",
    "vectors = model_lstm.get_weights()[0]\n",
    "for i in range(4,vocab_size):\n",
    "    f.write('{} {}\\n'.format(index_to_word[i], ' '.join(map(str, list(vectors[i, :])))))\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "floral-master",
   "metadata": {},
   "source": [
    "> pre-trained 모델을 가져올 때 생기는 에러로 gensim을 사용하는 순간부터 downgraded 된 버전으로 설치함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "expired-manhattan",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting gensim==3.8.3\n",
      "  Downloading gensim-3.8.3-cp37-cp37m-manylinux1_x86_64.whl (24.2 MB)\n",
      "\u001b[K     |████████████████████████████████| 24.2 MB 21.9 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: six>=1.5.0 in /opt/conda/lib/python3.7/site-packages (from gensim==3.8.3) (1.15.0)\n",
      "Requirement already satisfied: smart-open>=1.8.1 in /opt/conda/lib/python3.7/site-packages (from gensim==3.8.3) (4.1.2)\n",
      "Requirement already satisfied: numpy>=1.11.3 in /opt/conda/lib/python3.7/site-packages (from gensim==3.8.3) (1.19.5)\n",
      "Requirement already satisfied: scipy>=0.18.1 in /opt/conda/lib/python3.7/site-packages (from gensim==3.8.3) (1.4.1)\n",
      "Installing collected packages: gensim\n",
      "  Attempting uninstall: gensim\n",
      "    Found existing installation: gensim 4.0.1\n",
      "    Uninstalling gensim-4.0.1:\n",
      "      Successfully uninstalled gensim-4.0.1\n",
      "Successfully installed gensim-3.8.3\n",
      "\u001b[33mWARNING: You are using pip version 20.3.3; however, version 21.3.1 is available.\n",
      "You should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'3.8.3'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!pip install gensim==3.8.3\n",
    "\n",
    "import gensim\n",
    "\n",
    "gensim.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "casual-synthetic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.04241488, -0.00012171, -0.03687009,  0.02396451,  0.05803311,\n",
       "        0.03160533, -0.03439193,  0.06026774,  0.0729382 ,  0.08736394,\n",
       "        0.0439916 ,  0.03483137, -0.0745207 ,  0.03991708, -0.05535474,\n",
       "       -0.01540734,  0.05892337,  0.04202316, -0.02229529,  0.0108406 ,\n",
       "        0.01592908,  0.04492246, -0.03567177, -0.0053763 ,  0.02020527,\n",
       "        0.02136189,  0.02337406,  0.00956615, -0.04516129,  0.00602037,\n",
       "        0.01837775,  0.0428514 ], dtype=float32)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_vectors = gensim.models.keyedvectors.Word2VecKeyedVectors.load_word2vec_format(word2vec_file_path, binary=False)\n",
    "vector = word_vectors['영화']\n",
    "vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "charitable-remove",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('박자', 0.6198108792304993),\n",
       " ('스타일', 0.5904603004455566),\n",
       " ('갖', 0.5793795585632324),\n",
       " ('방법', 0.5746837854385376),\n",
       " ('\"\"\"', 0.5726088285446167),\n",
       " ('네요', 0.5686880946159363),\n",
       " ('수잔', 0.5651038885116577),\n",
       " ('짐캐리', 0.5484471321105957),\n",
       " ('입니다', 0.5463017225265503),\n",
       " ('냥', 0.5370578765869141)]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_vectors.similar_by_word('영화')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pregnant-group",
   "metadata": {},
   "source": [
    "> 딱 봐도 연관성이 없다....  \n",
    "> 혹시 다른 모델에 쓰인 임베딩 레이어는 다를까?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "south-inspector",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 32)\n"
     ]
    }
   ],
   "source": [
    "embedding_layer = model_1dcon.layers[0] # 모델 변경\n",
    "weights = embedding_layer.get_weights()[0]\n",
    "print(weights.shape)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cardiovascular-contact",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('으다', 0.6505240797996521),\n",
       " ('백', 0.6457682251930237),\n",
       " ('b', 0.6294980049133301),\n",
       " ('취미', 0.5602571368217468),\n",
       " ('스타워즈', 0.556815505027771),\n",
       " ('크기', 0.5542166829109192),\n",
       " ('굵', 0.5493003129959106),\n",
       " ('지극히', 0.5475529432296753),\n",
       " ('터지', 0.5426718592643738),\n",
       " ('미아', 0.5202059149742126)]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word2vec_file_path = os.getenv('HOME')+'/aiffel/sentiment_classification/data/practice2_word2vec.txt' # 다른 경로\n",
    "f = open(word2vec_file_path, 'w')\n",
    "f.write('{} {}\\n'.format(vocab_size-4, word_vector_dim))\n",
    "\n",
    "vectors = embedding_layer.get_weights()[0] \n",
    "for i in range(4,vocab_size):\n",
    "    f.write('{} {}\\n'.format(index_to_word[i], ' '.join(map(str, list(vectors[i, :])))))\n",
    "f.close()\n",
    "\n",
    "word_vectors = gensim.models.keyedvectors.Word2VecKeyedVectors.load_word2vec_format(word2vec_file_path, binary=False)\n",
    "word_vectors.similar_by_word('영화')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "square-conservation",
   "metadata": {},
   "source": [
    "> 오 왠지는 모르겠지만, 임베딩이 다르게 된다..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "classical-barrel",
   "metadata": {},
   "source": [
    "## # pre-trained 임베딩 레이어 가져오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "frozen-spouse",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# pre-trained word2vec 가져오기\n",
    "import os\n",
    "word2vec_path = os.getenv('HOME')+'/aiffel/sentiment_classification/data/ko.bin'\n",
    "word2vec = gensim.models.Word2Vec.load(word2vec_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "prime-cookbook",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<gensim.models.word2vec.Word2Vec at 0x7fc25c34a110>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 객체 확인\n",
    "word2vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "political-astrology",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(200,)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word2vec['영화'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "violent-colleague",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `similar_by_word` (Method will be removed in 4.0.0, use self.wv.similar_by_word() instead).\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('다큐멘터리', 0.7265259027481079),\n",
       " ('영화사', 0.715214192867279),\n",
       " ('드라마', 0.705294132232666),\n",
       " ('뮤지컬', 0.6947016716003418),\n",
       " ('코미디', 0.69093257188797),\n",
       " ('영화인', 0.6702202558517456),\n",
       " ('서부극', 0.6571458578109741),\n",
       " ('스릴러', 0.6533164978027344),\n",
       " ('로맨스', 0.6428799629211426),\n",
       " ('애니메이션', 0.6425570249557495)]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word2vec.similar_by_word('영화')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "included-armstrong",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:8: DeprecationWarning: Call to deprecated `__contains__` (Method will be removed in 4.0.0, use self.wv.__contains__() instead).\n",
      "  \n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:9: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "# 워드벡터에 있는 단어들의 vector 값을 복사\n",
    "vocab_size = 10000  # 어휘 사전 크기\n",
    "word_vector_dim = 200  # 워드 벡터의 차원수\n",
    "embedding_matrix = np.random.rand(vocab_size, word_vector_dim)\n",
    "\n",
    "# embedding_matrix에 Word2Vec 워드 벡터를 단어 하나씩마다 차례차례 카피한다.\n",
    "for i in range(4,vocab_size):\n",
    "    if index_to_word[i] in word2vec:\n",
    "        embedding_matrix[i] = word2vec[index_to_word[i]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cross-freedom",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 200)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding_matrix.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sixth-egyptian",
   "metadata": {},
   "source": [
    "### # 모델에 임베딩 레이어 넣어서 훈련 및 예측"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "comparative-chicago",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_4 (Embedding)      (None, None, 200)         2000000   \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 64)                67840     \n",
      "_________________________________________________________________\n",
      "dense_15 (Dense)             (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dense_16 (Dense)             (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 2,069,953\n",
      "Trainable params: 2,069,953\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 위의 RNN 모델과 word_vector_dim 제외하고 동일하게 세팅\n",
    "from tensorflow.keras.initializers import Constant\n",
    "\n",
    "vocab_size = 10000\n",
    "word_vector_dim = 200 \n",
    "\n",
    "model_lstm_new = tf.keras.Sequential()\n",
    "model_lstm_new.add(tf.keras.layers.Embedding(vocab_size,\n",
    "                                             word_vector_dim,\n",
    "                                             embeddings_initializer=Constant(embedding_matrix),\n",
    "                                             trainable=True,\n",
    "                                             input_length=max_len,\n",
    "                                             input_shape=(None,)))\n",
    "model_lstm_new.add(tf.keras.layers.LSTM(64))\n",
    "model_lstm_new.add(tf.keras.layers.Dense(32, activation='relu'))\n",
    "model_lstm_new.add(tf.keras.layers.Dense(1, activation='sigmoid'))  # 최종 출력은 긍정/부정을 나타내는 1dim 입니다.\n",
    "\n",
    "model_lstm_new.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "amended-conflict",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "229/229 - 10s - loss: 0.4726 - accuracy: 0.7668 - val_loss: 0.3804 - val_accuracy: 0.8301\n",
      "Epoch 2/10\n",
      "229/229 - 7s - loss: 0.3351 - accuracy: 0.8554 - val_loss: 0.3366 - val_accuracy: 0.8530\n",
      "Epoch 3/10\n",
      "229/229 - 7s - loss: 0.2919 - accuracy: 0.8771 - val_loss: 0.3262 - val_accuracy: 0.8591\n",
      "Epoch 4/10\n",
      "229/229 - 7s - loss: 0.2656 - accuracy: 0.8899 - val_loss: 0.3312 - val_accuracy: 0.8571\n",
      "Epoch 5/10\n",
      "229/229 - 7s - loss: 0.2388 - accuracy: 0.9030 - val_loss: 0.3336 - val_accuracy: 0.8578\n",
      "Epoch 6/10\n",
      "229/229 - 7s - loss: 0.2183 - accuracy: 0.9132 - val_loss: 0.3620 - val_accuracy: 0.8494\n",
      "Epoch 7/10\n",
      "229/229 - 7s - loss: 0.1988 - accuracy: 0.9225 - val_loss: 0.3571 - val_accuracy: 0.8592\n",
      "Epoch 8/10\n",
      "229/229 - 7s - loss: 0.1757 - accuracy: 0.9321 - val_loss: 0.3970 - val_accuracy: 0.8516\n",
      "Epoch 9/10\n",
      "229/229 - 7s - loss: 0.1565 - accuracy: 0.9413 - val_loss: 0.4008 - val_accuracy: 0.8564\n",
      "Epoch 10/10\n",
      "229/229 - 7s - loss: 0.1358 - accuracy: 0.9496 - val_loss: 0.4496 - val_accuracy: 0.8528\n"
     ]
    }
   ],
   "source": [
    "model_lstm_new.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model_lstm_new.fit(xx_train,\n",
    "                         yy_train,\n",
    "                         epochs=EPOCHS,\n",
    "                         batch_size=512,\n",
    "                        validation_data=(xx_validation, yy_validation),\n",
    "                        verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "unusual-abuse",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1537/1537 - 5s - loss: 0.4557 - accuracy: 0.8496\n",
      "[0.4556523263454437, 0.8495839834213257]\n"
     ]
    }
   ],
   "source": [
    "# test 셋으로 평가해보기\n",
    "results = model_lstm_new.evaluate(X_test_pre, y_test, verbose=2)\n",
    "\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "virgin-federal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 그래프로 그려보기\n",
    "\n",
    "acc = history.history['accuracy']\n",
    "loss = history.history['loss']\n",
    "val_acc = history.history['val_accuracy']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(acc, label='training')\n",
    "plt.plot(val_acc, 'o', label='validation')\n",
    "plt.xlabel('epocks')\n",
    "plt.ylabel('accuracy', fontsize=20)\n",
    "plt.legend(fontsize=20)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(loss, label='training')\n",
    "plt.plot(val_loss, 'o', label='validation')\n",
    "plt.xlabel('epocks')\n",
    "plt.ylabel('loss', fontsize=20)\n",
    "plt.legend(fontsize=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "amino-morris",
   "metadata": {},
   "source": [
    "> LSTM with new word2vec의 경우 위의 LSTM과 비교했을 때,(에포크 10)\n",
    ">  - train = 약 92% -> 95%, val = 약 84% -> 85%, test = 약 84% -> 85%로 정말 근소하게 개선됐다.\n",
    ">  - 사실 개선됐는지도 모르겠다.\n",
    ">  - validatoin 셋에서 보인 미비한 정확도 개선과 overfitting 스러움은 동일하게 남았다.\n",
    ">  - 어쩜 LSTM 한 층과 Dense layer로 구성된 모델은 좋은 품질의 word2vec이어도 어쩔 수 없는 모델상 한계가 있나보다.\n",
    ">  - 아이펠 코드에 있던 모델로 한 번 테스트해봐야겠다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bizarre-avenue",
   "metadata": {},
   "source": [
    "### # 모델 재설계 후 새로운 word2vec 효과 확인해보기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "muslim-cursor",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_5 (Embedding)      (None, None, 200)         2000000   \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, None, 16)          22416     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, None, 16)          0         \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, None, 16)          1808      \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_2 (Glob (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 8)                 136       \n",
      "_________________________________________________________________\n",
      "dense_18 (Dense)             (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 2,024,369\n",
      "Trainable params: 2,024,369\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 위에서 LSTM과 Dense 사이에 Conv1D -> MaxPooling -> Con1D -> Conv1D -> MaxPooling 추가\n",
    "model_final = tf.keras.Sequential()\n",
    "model_final.add(tf.keras.layers.Embedding(vocab_size,\n",
    "                                             word_vector_dim,\n",
    "                                             embeddings_initializer=Constant(embedding_matrix),\n",
    "                                             trainable=True,\n",
    "                                             input_length=max_len,\n",
    "                                             input_shape=(None,)))\n",
    "model_final.add(tf.keras.layers.Conv1D(16, 7, activation='relu'))\n",
    "model_final.add(tf.keras.layers.MaxPooling1D(5))\n",
    "model_final.add(tf.keras.layers.Conv1D(16, 7, activation='relu'))\n",
    "model_final.add(tf.keras.layers.GlobalMaxPooling1D())\n",
    "model_final.add(tf.keras.layers.Dense(8, activation='relu'))\n",
    "model_final.add(tf.keras.layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "model_final.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "emotional-watershed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "229/229 - 13s - loss: 0.5851 - accuracy: 0.6766 - val_loss: 0.4543 - val_accuracy: 0.7876\n",
      "Epoch 2/10\n",
      "229/229 - 6s - loss: 0.4016 - accuracy: 0.8193 - val_loss: 0.3956 - val_accuracy: 0.8231\n",
      "Epoch 3/10\n",
      "229/229 - 6s - loss: 0.3345 - accuracy: 0.8560 - val_loss: 0.3620 - val_accuracy: 0.8411\n",
      "Epoch 4/10\n",
      "229/229 - 6s - loss: 0.2978 - accuracy: 0.8751 - val_loss: 0.3633 - val_accuracy: 0.8436\n",
      "Epoch 5/10\n",
      "229/229 - 6s - loss: 0.2700 - accuracy: 0.8891 - val_loss: 0.3606 - val_accuracy: 0.8464\n",
      "Epoch 6/10\n",
      "229/229 - 6s - loss: 0.2461 - accuracy: 0.9012 - val_loss: 0.3696 - val_accuracy: 0.8463\n",
      "Epoch 7/10\n",
      "229/229 - 6s - loss: 0.2245 - accuracy: 0.9118 - val_loss: 0.3907 - val_accuracy: 0.8396\n",
      "Epoch 8/10\n",
      "229/229 - 6s - loss: 0.2038 - accuracy: 0.9217 - val_loss: 0.4117 - val_accuracy: 0.8384\n",
      "Epoch 9/10\n",
      "229/229 - 6s - loss: 0.1828 - accuracy: 0.9308 - val_loss: 0.4272 - val_accuracy: 0.8366\n",
      "Epoch 10/10\n",
      "229/229 - 6s - loss: 0.1602 - accuracy: 0.9418 - val_loss: 0.4526 - val_accuracy: 0.8398\n"
     ]
    }
   ],
   "source": [
    "model_final.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model_final.fit(xx_train,\n",
    "                         yy_train,\n",
    "                         epochs=EPOCHS,\n",
    "                         batch_size=512,\n",
    "                        validation_data=(xx_validation, yy_validation),\n",
    "                        verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "minor-mountain",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1537/1537 - 5s - loss: 0.4621 - accuracy: 0.8356\n",
      "[0.46211913228034973, 0.8356286883354187]\n"
     ]
    }
   ],
   "source": [
    "# test 셋으로 평가해보기\n",
    "results = model_final.evaluate(X_test_pre, y_test, verbose=2)\n",
    "\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "noted-cinema",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 그래프로 그려보기\n",
    "\n",
    "acc = history.history['accuracy']\n",
    "loss = history.history['loss']\n",
    "val_acc = history.history['val_accuracy']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(acc, label='training')\n",
    "plt.plot(val_acc, 'o', label='validation')\n",
    "plt.xlabel('epocks')\n",
    "plt.ylabel('accuracy', fontsize=20)\n",
    "plt.legend(fontsize=20)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(loss, label='training')\n",
    "plt.plot(val_loss, 'o', label='validation')\n",
    "plt.xlabel('epocks')\n",
    "plt.ylabel('loss', fontsize=20)\n",
    "plt.legend(fontsize=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "smoking-creature",
   "metadata": {},
   "source": [
    "> 새로운 Word2Vec과 LMS에 있던 모델 사용\n",
    ">  - train = 약 93%, validation = 약 83%, test = 약 83%\n",
    ">  - 결과적으로 새로운 word2vec으로도 내 지식 범위 안에 있는 수준에서는 test 데이터에서 90% 이상의 정확도를 내지는 못했다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "trained-smooth",
   "metadata": {},
   "source": [
    "## # 회고\n",
    "#### Point 1) 패키지 사용의 어려움\n",
    "- 이번 exploration에서 권고한 Mecab과 gensim은 여러모로 사용하기 불편했다.\n",
    "- Mecab은 설치가 안되어 있는듯 이 주피터노트북을 실행할 때마다 설치하라고 계속 오류를 냈고,\n",
    "- gensim은 LMS에서 알려주듯 처음붙 3.x.x대로 downgrade하고 시작을 했으나, 그래도 pre-trained 모델을 불러오는 데 오류를 냈었다.(이래저래 해결 방법을 찾다가 퍼실 분들의 예전 교육생 시절 주피터에 쓰인 코드를 보고 현재 LMS상 쓰여 있는 method명과 다르단 점을 발견했다. 그리고 변경하니 해결됐다)\n",
    "- 실무에 들어가면 이런 식으로 부딪히는 면이 많을 텐데, 이와 관련된 가상환경 구축(?)도 적응해야 할 듯 하다.\n",
    "\n",
    "#### Point 2) Word2Vec의 성능 확인\n",
    "- pre-trained 된 word2vec을 가져오면 학습과 예측에 굉장히 큰 개선이 있을 줄 알았다.\n",
    "- 어쩜 나의 코드상 문제일 수도 있는데, 여튼 별다른 정확도 개선이 이루어지지 않았다.\n",
    "- 가설로는 1) word2vec도 어쩔 수 없이 모델의 설계에 많이 영향을 받는다.\n",
    "- 2) 내 코드가 잘못되었다.\n",
    "- 근데 뭐가 됐든 word2vec의 상태를 뜯어보기엔 뭔가 많이 추상적이라는 생각이 들었다.\n",
    "\n",
    "#### Point 3) 한국말 pre-trained 모델의 부족\n",
    "- 위와 같은 어려움 때문에 사실 다른 모델을 찾아보려 했다.\n",
    "- 그러다 SKT에서 만든 KoBERT라는 걸 봤는데, 그건 학습이 너무 오래 걸렸고, 나중 가서는 메모리 부족으로 오류가 났다...\n",
    "\n",
    "#### 요약하면...\n",
    "일단 NLP라는 것을 조금씩 경험해보고 있다는 것에 의미를 두고, 그것이 어떤 단계들을 거쳐서(토큰화 -> 단어사전 구축 -> 패딩 -> word2vec 만들기(임베딩) -> 학습 및 예측) 진행되는지를 머릿속에 한 번 더 그려보면서 마무리해야겠다."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
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 },
 "nbformat": 4,
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