0302-klue-ml-tree-output.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "🌱 인프런 📚 모두의 한국어 텍스트 분석과 자연어처리 with 파이썬 🐍 https://inf.run/FX4TP\n",
    "\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/corazzon/python-text-analysis/blob/main/0302-klue-ml-tree-output.ipynb)\n",
    "\n",
    "\n",
    "\n",
    "## 연합뉴스 타이틀 주제 분류\n",
    "* 데이터셋 출처 : \n",
    "    * [뉴스 토픽 분류 AI 경진대회 - DACON](https://dacon.io/competitions/official/235747/overview/description)\n",
    "\n",
    "\n",
    "<img src=\"https://i.imgur.com/b6wcQ7f.png\" width=\"500\">\n",
    "\n",
    "\n",
    "## 기초 분류 모델 만들기\n",
    "\n",
    "* 데이터 로드\n",
    "* 데이터 전처리\n",
    "* 단어 벡터화(BOW, TF-IDF)\n",
    "* 분류기 설정하기\n",
    "* 분류기로 학습시키기\n",
    "* 학습의 정확도 보기\n",
    "* 테스트 데이터 예측하기\n",
    "* 실제 데이터와 예측결과의 차이를 보기\n",
    "\n",
    "<img src=\"https://i.imgur.com/rKOYbeX.png\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 라이브러리 로드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 분석을 위한 pandas, 수치계산을 위한 numpy, 시각화를 위한 seaborn, matplotlib 을 로드합니다.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 시각화를 위한 폰트 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install koreanize_matplotlib\n",
    "import koreanize_matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series([1, 3, 5, -7, 9]).plot(title=\"한글\", figsize=(6, 1));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 로드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((45654, 3), (9131, 2))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 학습, 예측 데이터셋을 불러옵니다.\n",
    "# train, test\n",
    "train = pd.read_csv(\"data/klue/train_data.csv\")\n",
    "test = pd.read_csv(\"data/klue/test_data.csv\")\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>title</th>\n",
       "      <th>topic_idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>인천→핀란드 항공기 결항…휴가철 여행객 분통</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>실리콘밸리 넘어서겠다…구글 15조원 들여 美전역 거점화</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>이란 외무 긴장완화 해결책은 미국이 경제전쟁 멈추는 것</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>NYT 클린턴 측근韓기업 특수관계 조명…공과 사 맞물려종합</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>시진핑 트럼프에 중미 무역협상 조속 타결 희망</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                             title  topic_idx\n",
       "0      0          인천→핀란드 항공기 결항…휴가철 여행객 분통          4\n",
       "1      1    실리콘밸리 넘어서겠다…구글 15조원 들여 美전역 거점화          4\n",
       "2      2    이란 외무 긴장완화 해결책은 미국이 경제전쟁 멈추는 것          4\n",
       "3      3  NYT 클린턴 측근韓기업 특수관계 조명…공과 사 맞물려종합          4\n",
       "4      4         시진핑 트럼프에 중미 무역협상 조속 타결 희망          4"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45654</td>\n",
       "      <td>유튜브 내달 2일까지 크리에이터 지원 공간 운영</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45655</td>\n",
       "      <td>어버이날 맑다가 흐려져…남부지방 옅은 황사</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45656</td>\n",
       "      <td>내년부터 국가RD 평가 때 논문건수는 반영 않는다</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45657</td>\n",
       "      <td>김명자 신임 과총 회장 원로와 젊은 과학자 지혜 모을 것</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45658</td>\n",
       "      <td>회색인간 작가 김동식 양심고백 등 새 소설집 2권 출간</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                            title\n",
       "0  45654       유튜브 내달 2일까지 크리에이터 지원 공간 운영\n",
       "1  45655          어버이날 맑다가 흐려져…남부지방 옅은 황사\n",
       "2  45656      내년부터 국가RD 평가 때 논문건수는 반영 않는다\n",
       "3  45657  김명자 신임 과총 회장 원로와 젊은 과학자 지혜 모을 것\n",
       "4  45658   회색인간 작가 김동식 양심고백 등 새 소설집 2권 출간"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>topic</th>\n",
       "      <th>topic_idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IT과학</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>경제</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>사회</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>생활문화</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>세계</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>스포츠</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>정치</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  topic  topic_idx\n",
       "0  IT과학          0\n",
       "1    경제          1\n",
       "2    사회          2\n",
       "3  생활문화          3\n",
       "4    세계          4\n",
       "5   스포츠          5\n",
       "6    정치          6"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 토픽을 불러옵니다.\n",
    "topic = pd.read_csv(\"data/klue/topic_dict.csv\")\n",
    "topic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 문자 전처리\n",
    "### 전처리 함수로 전처리 하기\n",
    "\n",
    "* [정규 표현식 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/%EC%A0%95%EA%B7%9C_%ED%91%9C%ED%98%84%EC%8B%9D)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 정규표현식 불러오기\n",
    "import re\n",
    "\n",
    "def preprocessing(text):\n",
    "    # 개행문자 제거\n",
    "    text = re.sub('\\\\\\\\n', ' ', text)\n",
    "    # 특수문자 제거\n",
    "    # 특수문자나 이모티콘 등은 때로는 의미를 갖기도 하지만 여기에서는 제거했습니다.\n",
    "    # text = re.sub('[?.,;:|\\)*~`’!^\\-_+<>@\\#$%&-=#}※]', '', text)\n",
    "    # 한글, 영문, 숫자만 남기고 모두 제거하도록 합니다.\n",
    "    # text = re.sub('[^가-힣ㄱ-ㅎㅏ-ㅣa-zA-Z0-9]', ' ', text)\n",
    "    # 한글, 영문만 남기고 모두 제거하도록 합니다.\n",
    "    text = re.sub('[^가-힣ㄱ-ㅎㅏ-ㅣa-zA-Z]', ' ', text)\n",
    "    # 중복으로 생성된 공백값을 제거합니다. \n",
    "    text = re.sub('[\\s]+', ' ', text)\n",
    "    # 영문자를 소문자로 만듭니다.\n",
    "    # CountVectorizer, TfidfVectorizer 를 사용하면 기본값이 소문자이고 구두점도 제거해주기 때문에\n",
    "    # 전처리를 하지 않아도 되지만 예시로 소개합니다.\n",
    "    text = text.lower()\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'전처리가 잘 되는지 확인 합니다 공 백도 확인하고 숫자 도 확인 합니다 english는 모두 소문자로 만듭니다 '"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 정규표현식 확인\n",
    "text = \"전처리가 잘~~~되는지 확인 합니다. 공    백도 확인하고 숫자0-9도 확인 합니다. EnglisH는 모두 소문자로 만듭니다.\"\n",
    "preprocessing(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# map을 통해 전처리 일괄 적용\n",
    "train[\"title\"] = train[\"title\"].map(preprocessing)\n",
    "test[\"title\"] = test[\"title\"].map(preprocessing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                인천 핀란드 항공기 결항 휴가철 여행객 분통\n",
       "1             실리콘밸리 넘어서겠다 구글 조원 들여 전역 거점화\n",
       "2          이란 외무 긴장완화 해결책은 미국이 경제전쟁 멈추는 것\n",
       "3        nyt 클린턴 측근 기업 특수관계 조명 공과 사 맞물려종합\n",
       "4               시진핑 트럼프에 중미 무역협상 조속 타결 희망\n",
       "                       ...               \n",
       "45649        kb금융 미국 ib 스티펠과 제휴 선진국 시장 공략\n",
       "45650      보 서울시교육청 신종코로나 확산에 개학 연기 휴업 검토\n",
       "45651              게시판 키움증권 키움 영웅전 실전투자대회\n",
       "45652                   답변하는 배기동 국립중앙박물관장\n",
       "45653        한국인터넷기자상 시상식 내달 일 개최 특별상 김성후\n",
       "Name: title, Length: 45654, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"title\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             유튜브 내달 일까지 크리에이터 지원 공간 운영\n",
       "1               어버이날 맑다가 흐려져 남부지방 옅은 황사\n",
       "2           내년부터 국가rd 평가 때 논문건수는 반영 않는다\n",
       "3       김명자 신임 과총 회장 원로와 젊은 과학자 지혜 모을 것\n",
       "4         회색인간 작가 김동식 양심고백 등 새 소설집 권 출간\n",
       "                     ...               \n",
       "9126            인천 오후 시 분 대설주의보 눈 cm 쌓여\n",
       "9127      노래방에서 지인 성추행 외교부 사무관 불구속 입건종합\n",
       "9128          년 전 부마항쟁 부산 시위 사진 점 최초 공개\n",
       "9129      게시판 아리랑tv 아프리카개발은행 총회 개회식 생중계\n",
       "9130    유영민 과기장관 강소특구는 지역 혁신의 중심 지원책 강구\n",
       "Name: title, Length: 9131, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[\"title\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 불용어 제거"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 불용어 제거\n",
    "# 불용어를 추가하여 어휘를 추가로 제거할 수 있습니다.\n",
    "def remove_stopwords(text):\n",
    "    tokens = text.split(' ')\n",
    "    stops = [ '합니다', '하는', '할', '하고', '한다', \n",
    "             '그리고', '입니다', '그', '등', '이런', '및','제', '더']\n",
    "    meaningful_words = [w for w in tokens if not w in stops]\n",
    "    return ' '.join(meaningful_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# map을 통해 불용어 일괄 제거\n",
    "train[\"title\"] = train[\"title\"].map(remove_stopwords)\n",
    "test[\"title\"] = test[\"title\"].map(remove_stopwords)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 학습 예측 데이터셋"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 학습, 예측 데이터셋 정의\n",
    "# 학습세트(예: 시험의 기출문제)\n",
    "# 예측세트(예: 실전 시험문제)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 정답값으로 사용할 topic_idx 를 변수에 담아 재사용 합니다.\n",
    "label_name = \"topic_idx\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        4\n",
       "1        4\n",
       "2        4\n",
       "3        4\n",
       "4        4\n",
       "        ..\n",
       "45649    1\n",
       "45650    2\n",
       "45651    1\n",
       "45652    2\n",
       "45653    2\n",
       "Name: topic_idx, Length: 45654, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 학습 세트의 정답\n",
    "y_train = train[label_name]\n",
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 벡터화\n",
    "* 머신러닝이나 딥러닝 알고리즘은 문자를 이해할 수 없습니다. 내부에서는 수치 계산이 이루어지기 때문에 문자를 숫자로 변경해 주어야 합니다.\n",
    "\n",
    "\n",
    "### CountVectorizer\n",
    "* CountVectorizer 는 사이킷런에서 제공하는 bag of words 를 만들 수 있는 방법입니다.\n",
    "* 텍스트 문서 모음을 토큰 수의 행렬로 변환합니다.\n",
    "\n",
    "* 단어들의 카운트(출현 빈도)로 여러 문서들을 벡터화\n",
    "* 문서목록에서 각 문서의 feature(문장의 특징) 노출수를 가중치로 설정한 BOW 벡터를 생성\n",
    "* 카운트 행렬, 단어 문서 행렬 (Term-Document Matrix, TDM))\n",
    "* max_df, min_df 인수를 사용하여 문서에서 토큰이 나타난 횟수를 기준으로 단어장을 구성할 수도 있음 \n",
    "* 토큰의 빈도가 max_df로 지정한 값을 초과 하거나 min_df로 지정한 값보다 작은 경우에는 무시하며 인자 값은 정수인 경우 횟수, 부동소수점인 경우 비율을 의미\n",
    "* API documentation:https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html\n",
    "\n",
    "### 사용법\n",
    "1. 문서를 토큰 리스트로 변환한다.\n",
    "2. 각 문서에서 토큰의 출현 빈도를 센다.\n",
    "3. 각 문서를 BOW 인코딩 벡터로 변환한다.\n",
    "4. 매개 변수\n",
    "    * analyzer : 단어, 문자 단위의 벡터화 방법 정의\n",
    "    * ngram_range : BOW 단위 수 (1, 3) 이라면 1개~3개까지 토큰을 묶어서 벡터화\n",
    "    * max_df : 어휘를 작성할 때 문서 빈도가 주어진 임계값보다 높은 용어(말뭉치 관련 불용어)는 제외 (기본값=1.0)\n",
    "        * max_df = 0.90 : 문서의 90% 이상에 나타나는 단어 제외\n",
    "        * max_df = 10 : 10개 이상의 문서에 나타나는 단어 제외\n",
    "    * min_df : 어휘를 작성할 때 문서 빈도가 주어진 임계값보다 낮은 용어는 제외합니다. 컷오프라고도 합니다.(기본값=1.0)\n",
    "        * min_df = 0.01 : 문서의 1% 미만으로 나타나는 단어 제외\n",
    "        * min_df = 10 : 문서에 10개 미만으로 나타나는 단어 제외\n",
    "    * stop_words : 불용어 정의"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>CountVectorizer(max_features=3000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer(max_features=3000)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "CountVectorizer(max_features=3000)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CountVectorizer 로 벡터화 합니다.\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cvect = CountVectorizer(max_features=3000) \n",
    "cvect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>CountVectorizer(max_features=3000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer(max_features=3000)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "CountVectorizer(max_features=3000)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvect.fit(train[\"title\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['afc', 'ag', 'ai', ..., '희생자', '힐러리', '힐만'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 단어 사전\n",
    "feature_names = cvect.get_feature_names_out()\n",
    "feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((45654, 3000), (9131, 3000))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 학습세트(예: 시험의 기출문제)\n",
    "# 예측세트(예: 실전 시험문제)\n",
    "# 변환\n",
    "# X_train, X_test\n",
    "X_train = cvect.transform(train[\"title\"])\n",
    "X_test = cvect.transform(test[\"title\"])\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<45654x3000 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 145741 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.sum 으로 위에서 구한 train_feature_vector 의 값을 모두 더합니다. axis=0 으로 합니다. \n",
    "df_train = pd.DataFrame(X_train.toarray(), columns=feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "대통령    1363\n",
       "억원      819\n",
       "게시판     734\n",
       "출시      734\n",
       "신간      671\n",
       "감독      625\n",
       "이란      618\n",
       "kt      580\n",
       "분기      555\n",
       "트럼프     546\n",
       "한국      508\n",
       "개발      455\n",
       "개최      450\n",
       "만에      436\n",
       "정부      421\n",
       "경기      415\n",
       "올해      391\n",
       "삼성      384\n",
       "코로나     382\n",
       "작년      376\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_sum = df_train.sum()\n",
    "train_sum.nlargest(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'train 단어 빈도 수'}>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 위에서 구한 빈도수를 그래프로 그립니다.\n",
    "train_sum.nlargest(50).plot(kind=\"bar\", figsize=(12, 3), title=\"train 단어 빈도 수\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = pd.DataFrame(X_test.toarray(), columns=feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "대통령    289\n",
       "게시판    190\n",
       "억원     162\n",
       "kt     144\n",
       "출시     135\n",
       "감독     120\n",
       "이란     120\n",
       "트럼프    118\n",
       "신간     111\n",
       "개최     109\n",
       "분기     109\n",
       "한국     102\n",
       "삼성      93\n",
       "네이버     89\n",
       "만에      89\n",
       "올해      82\n",
       "국내      81\n",
       "정부      78\n",
       "공개      76\n",
       "경기      75\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_sum = df_test.sum()\n",
    "test_sum.nlargest(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'test 단어 빈도 수'}>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_sum.nlargest(50).plot(kind=\"bar\", figsize=(12, 3), title=\"test 단어 빈도 수\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 모델 불러오기\n",
    "\n",
    "* [1.10. Decision Trees — scikit-learn documentation](https://scikit-learn.org/stable/modules/tree.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(random_state=42)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DecisionTreeClassifier 를 불러옵니다.\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model = DecisionTreeClassifier(random_state=42)\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 학습"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(random_state=42)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit 으로 학습시킵니다.\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 트리 알고리즘 분석하기\n",
    "* 의사결정나무를 시각화 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot_tree 로 시각화 합니다.\n",
    "from sklearn.tree import plot_tree\n",
    "\n",
    "pt = plot_tree(model, max_depth=5, \n",
    "               fontsize=5, \n",
    "               feature_names=feature_names, \n",
    "               filled=True, rounded=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 피처 중요도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "afc      4.290919e-04\n",
       "ag       1.680502e-04\n",
       "ai       4.698093e-03\n",
       "ai스피커    7.176695e-05\n",
       "ar       6.566712e-05\n",
       "             ...     \n",
       "흥행       1.485453e-04\n",
       "희망       1.306927e-04\n",
       "희생자      1.363400e-04\n",
       "힐러리      1.513524e-03\n",
       "힐만       6.037957e-07\n",
       "Length: 3000, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 피처 중요도가 높은 순으로 정렬해서 상위 10개만 봅니다.\n",
    "fi = pd.Series(model.feature_importances_)\n",
    "fi.index = feature_names\n",
    "fi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 피처 중요도를 막대그래프로 시각화 합니다.\n",
    "fi.nlargest(30).plot(kind=\"bar\", figsize=(12, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 예측"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 1, ..., 4, 2, 2])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict로 예측합니다. \n",
    "y_predict = model.predict(X_test)\n",
    "y_predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 제출 파일 불러오기\n",
    "* 답안지에 답을 옮겨 적고 제출하는 과정과 유사합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>topic_idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45654</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45655</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45656</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45657</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45658</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9126</th>\n",
       "      <td>54780</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9127</th>\n",
       "      <td>54781</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9128</th>\n",
       "      <td>54782</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9129</th>\n",
       "      <td>54783</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9130</th>\n",
       "      <td>54784</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9131 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index  topic_idx\n",
       "0     45654          0\n",
       "1     45655          0\n",
       "2     45656          0\n",
       "3     45657          0\n",
       "4     45658          0\n",
       "...     ...        ...\n",
       "9126  54780          0\n",
       "9127  54781          0\n",
       "9128  54782          0\n",
       "9129  54783          0\n",
       "9130  54784          0\n",
       "\n",
       "[9131 rows x 2 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sample_submission 파일을 불러옵니다.\n",
    "submit = pd.read_csv(\"data/klue/sample_submission.csv\")\n",
    "submit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>topic_idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45654</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45655</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45656</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45657</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45658</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9126</th>\n",
       "      <td>54780</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9127</th>\n",
       "      <td>54781</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9128</th>\n",
       "      <td>54782</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9129</th>\n",
       "      <td>54783</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9130</th>\n",
       "      <td>54784</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9131 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index  topic_idx\n",
       "0     45654          2\n",
       "1     45655          3\n",
       "2     45656          1\n",
       "3     45657          2\n",
       "4     45658          3\n",
       "...     ...        ...\n",
       "9126  54780          3\n",
       "9127  54781          6\n",
       "9128  54782          4\n",
       "9129  54783          2\n",
       "9130  54784          2\n",
       "\n",
       "[9131 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 예측 값을 답안지에 옮겨 담습니다.\n",
    "# 실제 시험을 보고 답안지에 답을 옮기는 과정과 유사합니다.\n",
    "submit[\"topic_idx\"] = y_predict\n",
    "submit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 제출을 위해 파일 저장"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = \"submission_dt.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 제출을 위해 파일 저장하기\n",
    "submit.to_csv(file_name, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "      <th>9126</th>\n",
       "      <td>54780</td>\n",
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       "    <tr>\n",
       "      <th>9127</th>\n",
       "      <td>54781</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9128</th>\n",
       "      <td>54782</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9129</th>\n",
       "      <td>54783</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9130</th>\n",
       "      <td>54784</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9131 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index  topic_idx\n",
       "0     45654          2\n",
       "1     45655          3\n",
       "2     45656          1\n",
       "3     45657          2\n",
       "4     45658          3\n",
       "...     ...        ...\n",
       "9126  54780          3\n",
       "9127  54781          6\n",
       "9128  54782          4\n",
       "9129  54783          2\n",
       "9130  54784          2\n",
       "\n",
       "[9131 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 잘 저장되었는지 확인하기\n",
    "pd.read_csv(file_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* dacon에 제출해서 리더보드 확인하기 : https://dacon.io/competitions/official/235747/mysubmission\n",
    "\n",
    "\n",
    "## 여러 방법을 통해 예측비율을 높여보세요. \n",
    "* 아래 항목 외의 기법을 사용해도 됩니다.\n",
    " * 전처리 하기\n",
    " * 불용어 처리\n",
    " * BOW, TF-IDF의 파라메터 변경\n",
    " * 분류기의 파라메터 변경\n",
    " * 분류기 변경"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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