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ftnext/word2vec入門.ipynb

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word2vecで彼氏と旦那の間に何があるのか聞いてみる ライブドアニュースコーパス編 #pyhack 20190406
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word2vec入門.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# word2vec入門\n",
"\n",
"ref: http://cedro3.com/ai/keras-mlp-livedoor/"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from janome.tokenizer import Tokenizer"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"sentence = 'すもももももももものうち'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"すもも\t名詞,一般,*,*,*,*,すもも,スモモ,スモモ\n",
"も\t助詞,係助詞,*,*,*,*,も,モ,モ\n",
"もも\t名詞,一般,*,*,*,*,もも,モモ,モモ\n",
"も\t助詞,係助詞,*,*,*,*,も,モ,モ\n",
"もも\t名詞,一般,*,*,*,*,もも,モモ,モモ\n",
"の\t助詞,連体化,*,*,*,*,の,ノ,ノ\n",
"うち\t名詞,非自立,副詞可能,*,*,*,うち,ウチ,ウチ\n"
]
}
],
"source": [
"words = Tokenizer().tokenize(sentence)\n",
"for word in words:\n",
" print(word)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"ja_tokenizer=Tokenizer()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['すもも', 'もも', 'もも', 'うち', '\\n']\n"
]
}
],
"source": [
"res = []\n",
"malist=ja_tokenizer.tokenize(sentence)\n",
"for tok in malist:\n",
" ps=tok.part_of_speech.split(\",\")[0]\n",
" if not ps in ['名詞']: continue\n",
" w=tok.base_form\n",
" if w==\"*\" or w==\"\": w=tok.surface\n",
" if w==\"\" or w==\"\\n\": continue\n",
" res.append(w)\n",
"res.append(\"\\n\")\n",
"print(res)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def ja_tokenize(text):\n",
" res=[]\n",
" lines=text.split(\"\\n\")\n",
" lines=lines[3:] # 最初の3行はヘッダー+題名なので捨てる\n",
" for line in lines:\n",
" malist=ja_tokenizer.tokenize(line)\n",
" for tok in malist:\n",
" ps=tok.part_of_speech.split(\",\")[0]\n",
" # 名詞でなければ以下の処理をスキップ\n",
" if not ps in ['名詞']: continue \n",
" w=tok.base_form\n",
" if w==\"*\" or w==\"\": w=tok.surface\n",
" if w==\"\" or w==\"\\n\": continue\n",
" res.append(w)\n",
" res.append(\"\\n\")\n",
" return res"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"path = './text/dokujo-tsushin/dokujo-tsushin-6915005.txt'\n",
"with open(path, 'r', encoding='utf-8') as f:\n",
" text = f.read()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1571"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(text)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"words=ja_tokenize(text)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"281"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(words)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['マナ', 'さん', '26', '歳', '塾', '講師', '顔立ち', '柳原', '可奈子', 'メイク']"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"words[0:10]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"text/movie-enter\n",
"movie-enter\n",
"----------------------------------------\n",
"text/it-life-hack\n",
"it-life-hack\n",
"----------------------------------------\n",
"text/kaden-channel\n",
"kaden-channel\n",
"----------------------------------------\n",
"text/topic-news\n",
"topic-news\n",
"----------------------------------------\n",
"text/livedoor-homme\n",
"livedoor-homme\n",
"----------------------------------------\n",
"text/peachy\n",
"peachy\n",
"----------------------------------------\n",
"text/sports-watch\n",
"sports-watch\n",
"----------------------------------------\n",
"text/dokujo-tsushin\n",
"dokujo-tsushin\n",
"----------------------------------------\n",
"text/CHANGES.txt\n",
"CHANGES.txt\n",
"----------------------------------------\n",
"text/README.txt\n",
"README.txt\n",
"----------------------------------------\n",
"text/smax\n",
"smax\n",
"----------------------------------------\n"
]
}
],
"source": [
"text_dir = 'text'\n",
"text_path = Path(text_dir)\n",
"for path in text_path.iterdir():\n",
" print(path)\n",
" print(path.name)\n",
" print('-'*40)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# 分かち書きしたテキストファイルを保存するフォルダを作る\n",
"dest_dir = 'wakati'\n",
"dest_dir_path = Path(dest_dir)\n",
"for path in text_path.iterdir():\n",
" if not path.is_dir():\n",
" continue\n",
" dest_folder_path = dest_dir_path / path.name\n",
" if not dest_folder_path.exists():\n",
" dest_folder_path.mkdir(parents=True)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"materials = next(text_path.iterdir()).iterdir()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PosixPath('text/movie-enter/movie-enter-5978741.txt')"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"next(materials)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"from tqdm import tqdm"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
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{
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},
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]
}
],
"source": [
"text_dir = 'text'\n",
"text_path = Path(text_dir)\n",
"for folder_path in text_path.iterdir():\n",
" if not folder_path.is_dir(): continue\n",
" for material_path in tqdm(folder_path.iterdir()):\n",
" # LICENSE.txtは以下の処理をスキップ\n",
" if material_path.name == 'LICENSE.txt': continue\n",
" with open(material_path, 'r', encoding='utf-8') as f:\n",
" text = f.read()\n",
" words = ja_tokenize(text)\n",
" wt = ' '.join(words)\n",
" # 例: ./wakati/dokujo-tsushin\n",
" wakati_dir_path = dest_dir_path / folder_path.name\n",
" wakati_path = wakati_dir_path / material_path.name\n",
" with open(wakati_path, 'w', encoding='utf-8') as f:\n",
" f.write(wt)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"# jupyterからだと(standard input)のためにうまくいかない\n",
"# !find wakati \\\n",
"# | grep -e movie-enter -e it-life-hack -e kaden-channel \\\n",
"# -e topic-news -e livedoor-homme -e peachy -e sports-watch \\\n",
"# -e dokujo-tsushin -e smax"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# !find wakati \\\n",
"# | grep -e movie-enter -e it-life-hack -e kaden-channel \\\n",
"# -e topic-news -e livedoor-homme -e peachy -e sports-watch \\\n",
"# -e dokujo-tsushin -e smax \\\n",
"# | awk '{system(\"cat \"$0\" >> livedoor_wakati.txt\")}'"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-04-06 14:46:51,191 : INFO : collecting all words and their counts\n",
"2019-04-06 14:46:51,194 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
"2019-04-06 14:46:51,773 : INFO : collected 61580 word types from a corpus of 1949826 raw words and 195 sentences\n",
"2019-04-06 14:46:51,774 : INFO : Loading a fresh vocabulary\n",
"2019-04-06 14:46:51,818 : INFO : effective_min_count=20 retains 9516 unique words (15% of original 61580, drops 52064)\n",
"2019-04-06 14:46:51,819 : INFO : effective_min_count=20 leaves 1759701 word corpus (90% of original 1949826, drops 190125)\n",
"2019-04-06 14:46:51,843 : INFO : deleting the raw counts dictionary of 61580 items\n",
"2019-04-06 14:46:51,847 : INFO : sample=0.001 downsamples 28 most-common words\n",
"2019-04-06 14:46:51,848 : INFO : downsampling leaves estimated 1664303 word corpus (94.6% of prior 1759701)\n",
"2019-04-06 14:46:51,872 : INFO : estimated required memory for 9516 words and 200 dimensions: 19983600 bytes\n",
"2019-04-06 14:46:51,873 : INFO : resetting layer weights\n",
"2019-04-06 14:46:51,994 : INFO : training model with 3 workers on 9516 vocabulary and 200 features, using sg=0 hs=0 sample=0.001 negative=5 window=15\n",
"2019-04-06 14:46:53,001 : INFO : EPOCH 1 - PROGRESS: at 65.13% examples, 1070778 words/s, in_qsize 5, out_qsize 0\n",
"2019-04-06 14:46:53,568 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
"2019-04-06 14:46:53,585 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
"2019-04-06 14:46:53,590 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
"2019-04-06 14:46:53,591 : INFO : EPOCH - 1 : training on 1949826 raw words (1664312 effective words) took 1.6s, 1044659 effective words/s\n",
"2019-04-06 14:46:54,600 : INFO : EPOCH 2 - PROGRESS: at 64.10% examples, 1051195 words/s, in_qsize 5, out_qsize 0\n",
"2019-04-06 14:46:55,113 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
"2019-04-06 14:46:55,128 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
"2019-04-06 14:46:55,133 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
"2019-04-06 14:46:55,134 : INFO : EPOCH - 2 : training on 1949826 raw words (1664463 effective words) took 1.5s, 1081408 effective words/s\n",
"2019-04-06 14:46:56,140 : INFO : EPOCH 3 - PROGRESS: at 64.62% examples, 1061541 words/s, in_qsize 5, out_qsize 0\n",
"2019-04-06 14:46:56,675 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
"2019-04-06 14:46:56,677 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
"2019-04-06 14:46:56,696 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
"2019-04-06 14:46:56,697 : INFO : EPOCH - 3 : training on 1949826 raw words (1664195 effective words) took 1.6s, 1066382 effective words/s\n",
"2019-04-06 14:46:57,707 : INFO : EPOCH 4 - PROGRESS: at 65.64% examples, 1076676 words/s, in_qsize 5, out_qsize 0\n",
"2019-04-06 14:46:58,221 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
"2019-04-06 14:46:58,234 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
"2019-04-06 14:46:58,244 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
"2019-04-06 14:46:58,244 : INFO : EPOCH - 4 : training on 1949826 raw words (1663988 effective words) took 1.5s, 1078517 effective words/s\n",
"2019-04-06 14:46:59,260 : INFO : EPOCH 5 - PROGRESS: at 63.08% examples, 1027948 words/s, in_qsize 5, out_qsize 0\n",
"2019-04-06 14:46:59,839 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
"2019-04-06 14:46:59,844 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
"2019-04-06 14:46:59,860 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
"2019-04-06 14:46:59,860 : INFO : EPOCH - 5 : training on 1949826 raw words (1664223 effective words) took 1.6s, 1031825 effective words/s\n",
"2019-04-06 14:46:59,861 : INFO : training on a 9749130 raw words (8321181 effective words) took 7.9s, 1058091 effective words/s\n",
"2019-04-06 14:46:59,862 : INFO : saving Word2Vec object under ./livedoor.model, separately None\n",
"2019-04-06 14:46:59,863 : INFO : not storing attribute vectors_norm\n",
"2019-04-06 14:46:59,864 : INFO : not storing attribute cum_table\n",
"2019-04-06 14:47:00,010 : INFO : saved ./livedoor.model\n"
]
}
],
"source": [
"from gensim.models import word2vec\n",
"import logging\n",
"\n",
"logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n",
"sentences = word2vec.Text8Corpus('./livedoor_wakati.txt')\n",
"\n",
"model = word2vec.Word2Vec(sentences, size=200, min_count=20, window=15)\n",
"model.save(\"./livedoor.model\")"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-04-06 14:48:31,601 : INFO : loading Word2Vec object from ./livedoor.model\n",
"2019-04-06 14:48:31,731 : INFO : loading wv recursively from ./livedoor.model.wv.* with mmap=None\n",
"2019-04-06 14:48:31,732 : INFO : setting ignored attribute vectors_norm to None\n",
"2019-04-06 14:48:31,733 : INFO : loading vocabulary recursively from ./livedoor.model.vocabulary.* with mmap=None\n",
"2019-04-06 14:48:31,734 : INFO : loading trainables recursively from ./livedoor.model.trainables.* with mmap=None\n",
"2019-04-06 14:48:31,734 : INFO : setting ignored attribute cum_table to None\n",
"2019-04-06 14:48:31,735 : INFO : loaded ./livedoor.model\n",
"2019-04-06 14:48:31,753 : INFO : precomputing L2-norms of word weight vectors\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"('スクール', 0.8470771312713623)\n",
"('長', 0.7549630999565125)\n",
"('主宰', 0.751012921333313)\n",
"('インストラクター', 0.7248972058296204)\n",
"('経歴', 0.7157208919525146)\n",
"('マーケティング', 0.7122070789337158)\n",
"('オーガニックコットン', 0.7119002938270569)\n",
"('プロフィール', 0.7104286551475525)\n",
"('深山', 0.7022006511688232)\n",
"('講座', 0.7012648582458496)\n"
]
}
],
"source": [
"from gensim.models import word2vec\n",
"\n",
"model = word2vec.Word2Vec.load(\"./livedoor.model\")\n",
"results = model.wv.most_similar(positive=['講師'])\n",
"for result in results:\n",
" print(result)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"def similar_words(word):\n",
" results = model.wv.most_similar(positive=[word])\n",
" for result in results:\n",
" print(result)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('お母さん', 0.9037052989006042)\n",
"('旦那', 0.8909508585929871)\n",
"('嫁', 0.87724369764328)\n",
"('嫁さん', 0.8716167211532593)\n",
"('付き合い', 0.8642361760139465)\n",
"('結局', 0.859423041343689)\n",
"('喧嘩', 0.8545925617218018)\n",
"('ケンカ', 0.8488543033599854)\n",
"('お世話', 0.8465662598609924)\n",
"('母親', 0.8399787545204163)\n"
]
}
],
"source": [
"similar_words('奥さん')"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('赤裸々', 0.7537071108818054)\n",
"('告白', 0.7462800741195679)\n",
"('男の子', 0.7415393590927124)\n",
"('願望', 0.7088780403137207)\n",
"('性格', 0.7034911513328552)\n",
"('恋人', 0.7020044326782227)\n",
"('年上', 0.7005584239959717)\n",
"('目撃', 0.6987625956535339)\n",
"('後輩', 0.6982093453407288)\n",
"('仲', 0.6962008476257324)\n"
]
}
],
"source": [
"similar_words('彼女')"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('パチンコ', 0.6943840384483337),\n",
" ('費用', 0.6358822584152222),\n",
" ('工作', 0.6315792798995972),\n",
" ('保護', 0.6136513948440552),\n",
" ('高額', 0.6085076332092285),\n",
" ('みそ', 0.6064600944519043),\n",
" ('母乳', 0.6042436361312866),\n",
" ('ヨネスケ', 0.6010379195213318),\n",
" ('家事', 0.5969640612602234),\n",
" ('我慢', 0.5961788296699524)]"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 奥さん - 彼女\n",
"model.wv.most_similar(positive=['奥さん'], negative=['彼女'])"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('喧嘩', 0.7948451638221741),\n",
" ('苦笑', 0.7869833707809448),\n",
" ('ダメ', 0.7780839204788208),\n",
" ('オレ', 0.7774254083633423),\n",
" ('結局', 0.7708460092544556),\n",
" ('ケンカ', 0.7655015587806702),\n",
" ('普通', 0.7626781463623047),\n",
" ('嫌い', 0.7605693340301514),\n",
" ('旦那', 0.7594084739685059),\n",
" ('こっち', 0.7576255798339844)]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 奥さん - 女 + 男 -> 旦那がある\n",
"model.wv.most_similar(positive=['奥さん','男'], negative=['女'])"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('農家', 0.6657891273498535),\n",
" ('あさみ', 0.6609635949134827),\n",
" ('作り', 0.6433599591255188),\n",
" ('アイディア', 0.6335129737854004),\n",
" ('色々', 0.6208319664001465),\n",
" ('工作', 0.6179128885269165),\n",
" ('丁寧', 0.6122527718544006),\n",
" ('土地', 0.612001895904541),\n",
" ('畑', 0.6064431071281433),\n",
" ('新鮮', 0.6032482385635376)]"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 旦那 - 彼氏\n",
"model.wv.most_similar(positive=['旦那'], negative=['彼氏'])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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