{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "

\n", "# **Project4 | word2vec**\n", "

\n", "## **1 데이터 전처리**" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'박두만\\n서태윤\\n조용구\\n권귀옥\\n구희봉\\n신동철\\n백광호20대 초반. 정박아\\n조병순30대 후반. 변태성향\\n박해일20대 초반. 공장 노동자. ‘유력한 용의자’.\\n선본 남자, 동네 양아치들... 등등\\n30대 초반의 전직 간호조무사, 마을 ‘야매주사’ 여인.\\n안송여중 1학년 학생\\n소현의 단짝 친구\\n박보희, 이향숙, 독고현순, 박명자, 안미선.\\n화면 가득 한 남자 아이의 얼굴이 보여진다.\\n쏟아지는 햇살 아래, 맑은 눈빛의 아이는 카메라 정면을 응시하고 있다.\\n아이는 코스모스 위에 앉아있는 잠자리를 향해 살며시 손을 뻗는다.\\n휙~ 잠자리가 날아가 버리면 ... 끝없이 펼쳐진 가을 논과 푸른 하늘이 보인다.\\n어렴풋이 들려오는 기계소리에 아이가 고개를 돌리면, 저 멀리 경운기 한 대가 오는 것이 보인다. 점점 커지는 소리와 함께, 경운기를 운전하는 노인네와 뒷칸에 \\n올라 탄 한 남자의 모습이 보인다.\\n스포츠 머리에 건달같은 인상을 풍기는 30대 남자 ... 형사 박두만이다.\\n경운기를 세운 노인은 소리를'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 살인의 추억 텍스트 불러오기\n", "filename = '../data/movie_memories_of_murder_2003.txt'\n", "with open(filename, 'r') as f:\n", " texts = f.read()\n", "texts[:500]" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 11.4 s, sys: 150 ms, total: 11.5 s\n", "Wall time: 5.45 s\n" ] }, { "data": { "text/plain": [ "'박두만\\n 서태윤\\n 조용구\\n 권귀옥\\n 구희봉\\n 신동철\\n 백광호 초반 정박\\n 조병순대 후반 변태성향\\n 박해일 초반 공장 노동자 유력 용의자\\n 선본 남자 동네 양아치 등등\\n 초반 전직 간호조무사 마을 야매주사 여인\\n 안송여중 학년 학생\\n 소현 단짝 친구\\n 박보희 이향숙 독고현순 박명자 안미선\\n 화면 가득 남자 아이 얼굴\\n 햇살 아래 맑은 눈빛 아이 카메라 정면 응시\\n 아이 코스모스 잠자리\\n 잠자리 가을 푸른 하늘\\n 기계소리 아이 고개 멀리 경운기 대가 점점 소리 경운기 운전 노인 뒷칸\\n 남자 모습\\n 스포츠 머리 건달 인상 풍기 남자 형사 박두만\\n 경운기 세운 노인 소리 아이 아이 아랑곳 노인 박두 안내 농수로 배수관\\n 화면 등장 여자 시체\\n 배수관 어두운 구멍 나체 여자 시신\\n 미니 후레쉬 주머니 여자 얼굴 박두만\\n 여자 순간 안광이 번뜩\\n 위로 파리 개미 토사물 거기 핏자국 새소리 햇살아래 하늘거리 코스모스\\n 무덤덤 여자 박보희 시체 뭔가 생경 느낌\\n 언제 주위 동네 아이\\n 녀석 여자 '" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from txtutil import txtnoun\n", "skips = {'두만':'박두만', '태윤':'서태윤', '용구':'조용구', '귀옥':'권귀옥', \n", " '희봉':'구희봉', '동철':'신동철', '광호':'백광호', '병순':'조병순', \n", " '해일':'박해일', '광호의':'백광호', '백광호의':'백광호'}\n", "%time texts = txtnoun(filename, skip=skips, tags=['Noun'])\n", "texts[:500]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# 명사 Token 작업된 자료를 ssResport.txt 로 저장 \n", "texts_file = '../data/mom_noun_script.txt'\n", "with open(texts_file, 'w', encoding='utf-8') as file:\n", " file.write(texts)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# ! cat ./data/ssResport.txt | head -n 10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

\n", "## **2 Word 2 vec 객체 만들기**\n", "gensim" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# ! pip3 install gensim" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "model saved.\n", "CPU times: user 3.14 s, sys: 101 ms, total: 3.24 s\n", "Wall time: 3.17 s\n" ] } ], "source": [ "%%time\n", "texts_file = '../data/mom_noun_script.txt'\n", "\n", "from gensim.models import word2vec\n", "data = word2vec.LineSentence(texts_file)\n", "model = word2vec.Word2Vec(data, size=30, window=2, min_count=10, hs=1,\n", " workers=4, iter=100, sg=1)\n", "model.save(\"../data/mom_script.model\")\n", "print(\"model saved.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

\n", "## **3 저장된 모델 불러오기 및 확인**\n", "gensim" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Once deleted, variables cannot be recovered. Proceed (y/[n])? y\n" ] } ], "source": [ "%reset" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Interactive namespace is empty.\n" ] } ], "source": [ "%who" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "149" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from gensim.models import word2vec\n", "model = word2vec.Word2Vec.load('../data/mom_script.model')\n", "len(model.wv.vocab.keys())" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['서태윤',\n", " '얼굴',\n", " '박두만',\n", " '여자',\n", " '백광호',\n", " '조용구',\n", " '박해일',\n", " '반장',\n", " '계속',\n", " '순간',\n", " '모습',\n", " '소현',\n", " '하나',\n", " '소리',\n", " '시작',\n", " '범인',\n", " '남자',\n", " '사람',\n", " '표정',\n", " '형사',\n", " '다시',\n", " '뭔가',\n", " '권귀옥',\n", " '지금',\n", " '고개',\n", " '잠시',\n", " '아이',\n", " '보고',\n", " '눈빛',\n", " '설영',\n", " '여기',\n", " '얘기',\n", " '동네',\n", " '화면',\n", " '시체',\n", " '시선',\n", " '사진',\n", " '한번',\n", " '서류',\n", " '위로',\n", " '책상',\n", " '새끼',\n", " '그림자',\n", " '멀리',\n", " '점점',\n", " '머리',\n", " '갑자기',\n", " '이향숙',\n", " '문득',\n", " '사건',\n", " '발자국',\n", " '자기',\n", " '가운데',\n", " '당신',\n", " '아래',\n", " '박두',\n", " '괴남자',\n", " '저기',\n", " '스타킹',\n", " '그냥',\n", " '목소리',\n", " '불빛',\n", " '사무실',\n", " '진짜',\n", " '모두',\n", " '운동화',\n", " '우리',\n", " '사이',\n", " '용의자',\n", " '현장',\n", " '조그만',\n", " '다른',\n", " '생각',\n", " '전경',\n", " '기차',\n", " '마주',\n", " '취조실',\n", " '바로',\n", " '발견',\n", " '임마',\n", " '잠깐',\n", " '박명자',\n", " '거기',\n", " '음악',\n", " '부분',\n", " '시점',\n", " '간다',\n", " '그거',\n", " '끄덕',\n", " '자리',\n", " '버럭',\n", " '우산',\n", " '변소',\n", " '어디',\n", " '정말',\n", " '잔뜩',\n", " '다리',\n", " '그대로',\n", " '똑바로',\n", " '서서히',\n", " '가득',\n", " '인상',\n", " '후레쉬',\n", " '너머',\n", " '순경',\n", " '저녁',\n", " '경찰',\n", " '훈련',\n", " '언덕',\n", " '구희봉',\n", " '카메라',\n", " '주위',\n", " '브래지어',\n", " '분위기',\n", " '아저씨',\n", " '구석',\n", " '피해자',\n", " '그게',\n", " '팬티',\n", " '어리둥절',\n", " '누군가',\n", " '라디오',\n", " '짜증',\n", " '의경',\n", " '방송',\n", " '빗줄기',\n", " '어둠속',\n", " '인부',\n", " '조병순',\n", " '유전자',\n", " '바람',\n", " '서로',\n", " '주변',\n", " '참깨밭',\n", " '근처',\n", " '듯이',\n", " '가방',\n", " '코피',\n", " '휴지',\n", " '권기옥',\n", " '기억',\n", " '자신',\n", " '이건',\n", " '신문',\n", " '조심스레',\n", " '빗소리',\n", " '빗물',\n", " '언덕집',\n", " '총구']" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(model.wv.index2word)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

\n", "## **4 Word2Vec 모델 내용 확인**\n", "gensim" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/markbaum/Python/nltk/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n", " if np.issubdtype(vec.dtype, np.int):\n" ] }, { "data": { "text/plain": [ "[('우리', 0.7900596857070923),\n", " ('거기', 0.6960532665252686),\n", " ('그냥', 0.6365386843681335),\n", " ('그거', 0.6006745100021362),\n", " ('변소', 0.5372765064239502),\n", " ('여기', 0.5345128774642944),\n", " ('얘기', 0.529381513595581),\n", " ('우산', 0.5198854207992554),\n", " ('한번', 0.5091301202774048),\n", " ('당신', 0.49215778708457947)]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.wv.most_similar(positive=['범인'])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/markbaum/Python/nltk/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n", " if np.issubdtype(vec.dtype, np.int):\n" ] }, { "data": { "text/plain": [ "[('어리둥절', 0.20564770698547363),\n", " ('괴남자', 0.14735087752342224),\n", " ('인부', 0.12758076190948486),\n", " ('끄덕', 0.0938890278339386),\n", " ('형사', 0.08171150088310242),\n", " ('박해일', 0.05804525688290596),\n", " ('시선', 0.054364465177059174),\n", " ('멀리', 0.05124831199645996),\n", " ('똑바로', 0.040952324867248535),\n", " ('조심스레', 0.04004107415676117)]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.wv.most_similar(negative=['범인'])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/markbaum/Python/nltk/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n", " if np.issubdtype(vec.dtype, np.int):\n" ] }, { "data": { "text/plain": [ "[('이향숙', 0.6904958486557007),\n", " ('참깨밭', 0.6747854948043823),\n", " ('의경', 0.633386492729187),\n", " ('발견', 0.6128952503204346),\n", " ('새끼', 0.6061909198760986),\n", " ('순경', 0.567891538143158),\n", " ('정말', 0.5417141914367676),\n", " ('스타킹', 0.5283291935920715),\n", " ('조그만', 0.5013279914855957),\n", " ('하나', 0.4856257438659668)]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.wv.most_similar(positive=['피해자'])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/markbaum/Python/nltk/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n", " if np.issubdtype(vec.dtype, np.int):\n" ] }, { "data": { "text/plain": [ "[('너머', 0.23135656118392944),\n", " ('총구', 0.19893616437911987),\n", " ('보고', 0.15661761164665222),\n", " ('기차', 0.13491174578666687),\n", " ('라디오', 0.13442900776863098),\n", " ('그대로', 0.13437345623970032),\n", " ('짜증', 0.12772715091705322),\n", " ('듯이', 0.08391261845827103),\n", " ('책상', 0.0827057808637619),\n", " ('빗물', 0.06605809181928635)]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.wv.most_similar(negative=['피해자'])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/markbaum/Python/nltk/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n", " if np.issubdtype(vec.dtype, np.int):\n" ] }, { "data": { "text/plain": [ "[('서태윤', 0.6975997686386108),\n", " ('백광호', 0.6874744892120361),\n", " ('뭔가', 0.6816097497940063),\n", " ('조용구', 0.6327073574066162),\n", " ('사진', 0.6286661624908447),\n", " ('휴지', 0.6270521879196167),\n", " ('표정', 0.6174828410148621),\n", " ('얼굴', 0.6163499355316162),\n", " ('고개', 0.6044750213623047),\n", " ('어리둥절', 0.5962847471237183)]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.wv.most_similar(positive=['박두만'])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/markbaum/Python/nltk/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n", " if np.issubdtype(vec.dtype, np.int):\n" ] }, { "data": { "text/plain": [ "[('박두만', 0.6975998282432556),\n", " ('박해일', 0.6611736416816711),\n", " ('서서히', 0.6190056800842285),\n", " ('권귀옥', 0.6106432676315308),\n", " ('반장', 0.5893822908401489),\n", " ('잠시', 0.584406316280365),\n", " ('뭔가', 0.5732418298721313),\n", " ('다시', 0.5702092051506042),\n", " ('취조실', 0.5583451986312866),\n", " ('보고', 0.5568782687187195)]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.wv.most_similar(positive=['서태윤'])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/markbaum/Python/nltk/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n", " if np.issubdtype(vec.dtype, np.int):\n" ] }, { "data": { "text/plain": [ "[('보고', 0.7067176699638367),\n", " ('구희봉', 0.6660506725311279),\n", " ('자리', 0.6340445280075073),\n", " ('박두만', 0.6327073574066162),\n", " ('짜증', 0.6230899095535278),\n", " ('백광호', 0.6188753843307495),\n", " ('사무실', 0.6172367930412292),\n", " ('갑자기', 0.5982691049575806),\n", " ('모두', 0.5936163663864136),\n", " ('잠시', 0.5735465884208679)]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.wv.most_similar(positive=['조용구'])" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/markbaum/Python/nltk/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n", " if np.issubdtype(vec.dtype, np.int):\n" ] }, { "data": { "text/plain": [ "[('뭔가', 0.68101966381073),\n", " ('박해일', 0.6716170907020569),\n", " ('백광호', 0.6408335566520691),\n", " ('조용구', 0.6297107934951782),\n", " ('반장', 0.629507303237915),\n", " ('고개', 0.6279381513595581),\n", " ('휴지', 0.6262889504432678),\n", " ('잠시', 0.620336651802063),\n", " ('얼굴', 0.618887186050415),\n", " ('듯이', 0.6011371612548828)]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.wv.most_similar(positive=['박두만','서태윤'])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/markbaum/Python/nltk/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n", " if np.issubdtype(vec.dtype, np.int):\n" ] }, { "data": { "text/plain": [ "[('박해일', 0.6344929933547974),\n", " ('보고', 0.6293025016784668),\n", " ('총구', 0.6223453283309937),\n", " ('듯이', 0.6176120042800903),\n", " ('휴지', 0.5986297726631165),\n", " ('반장', 0.5743878483772278),\n", " ('그대로', 0.5628871917724609),\n", " ('잠시', 0.5429807305335999),\n", " ('다시', 0.5402548313140869),\n", " ('짜증', 0.5381396412849426)]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.wv.most_similar(positive=['박두만','서태윤'],\n", " negative=['피해자'])" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/markbaum/Python/nltk/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n", " if np.issubdtype(vec.dtype, np.int):\n" ] }, { "data": { "text/plain": [ "[('자리', 0.6815916895866394),\n", " ('반장', 0.6703791618347168),\n", " ('뭔가', 0.6372842788696289),\n", " ('조용구', 0.6234527826309204),\n", " ('코피', 0.6135426759719849),\n", " ('표정', 0.6081563830375671),\n", " ('구석', 0.6037955284118652),\n", " ('짜증', 0.599467396736145),\n", " ('휴지', 0.5962086915969849),\n", " ('인부', 0.5915132761001587)]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.wv.most_similar(positive=['박두만','서태윤'],\n", " negative=['박해일'])" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/markbaum/Python/nltk/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n", " if np.issubdtype(vec.dtype, np.int):\n" ] }, { "data": { "text/plain": [ "[('박해일', 0.6704642176628113),\n", " ('어리둥절', 0.6535400748252869),\n", " ('잠시', 0.5683966279029846),\n", " ('순간', 0.5675263404846191),\n", " ('취조실', 0.5628403425216675),\n", " ('고개', 0.5516542792320251),\n", " ('조용구', 0.5489233732223511),\n", " ('괴남자', 0.5390815734863281),\n", " ('백광호', 0.5236437320709229),\n", " ('얼굴', 0.521577000617981)]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.wv.most_similar(positive=['박두만','서태윤'],\n", " negative=['범인'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

\n", "## **5 Visulaization**\n", "gensim" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['박두만', '서태윤', '조용구', '권귀옥', '구희봉', '백광호', '박해일', '용의자', '남자', '동네']" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(model.wv.vocab.keys())[:10]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/markbaum/Python/nltk/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n", " This is separate from the ipykernel package so we can avoid doing imports until\n" ] }, { "data": { "text/plain": [ "(149, 30)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# model.wv.vocab : { word: object of numeric vector }\n", "vocab = list(model.wv.vocab)\n", "X = model[vocab]\n", "X.shape" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.94 s, sys: 386 ms, total: 2.33 s\n", "Wall time: 2.11 s\n" ] } ], "source": [ "%%time\n", "from sklearn.manifold import TSNE\n", "tsne = TSNE(n_components= 2)\n", "X_tsne = tsne.fit_transform(X)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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박두만-0.4079280.991980
서태윤-0.8135010.597934
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권귀옥0.5038810.165505
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" ], "text/plain": [ " x y\n", "박두만 -0.407928 0.991980\n", "서태윤 -0.813501 0.597934\n", "조용구 -0.787828 1.725268\n", "권귀옥 0.503881 0.165505\n", "구희봉 -2.001381 3.504587" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "df = pd.DataFrame(X_tsne, \n", " index = vocab, \n", " columns=['x', 'y'])\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from matplotlib import rc\n", "rc('font', family=['NanumGothic','Malgun Gothic'])\n", "\n", "import matplotlib.pyplot as plt\n", "fig = plt.figure(figsize=(12,12))\n", "ax = fig.add_subplot(1, 1, 1)\n", "ax.scatter(df['x'], df['y'])\n", "for word, pos in df.iterrows():\n", " ax.annotate(word, pos, fontsize=15)\n", "plt.grid(True)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }