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Created October 7, 2015 11:30
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section10.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"http://www.cl.ecei.tohoku.ac.jp/nlp100/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 第10章: ベクトル空間法 (II)\n",
"第10章では，前章に引き続き単語ベクトルの学習に取り組む．"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting section10.py\n"
]
}
],
"source": [
"%%file section10.py\n",
"#!/usr/bin/env python\n",
"# -*- coding: utf-8 -*-\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.externals import joblib\n",
"from sklearn.metrics.pairwise import cosine_similarity\n",
"\n",
"class Word2Vec:\n",
"\n",
" def __init__(self):\n",
" self.V = None\n",
" self.i2w = dict()\n",
" self.w2i = dict()\n",
"\n",
" def __getitem__(self, word):\n",
" while True:\n",
" try:\n",
" i = self.w2i[word]\n",
" return self.V[i]\n",
" except KeyError:\n",
" # 末尾を1文字ずつ削っていけばいつかは見つかるはず\n",
" word = word[:-1]\n",
" \n",
" def get_matrix(self):\n",
" return self.V\n",
" \n",
" def get_rows(self):\n",
" return self.w2i.keys()\n",
" \n",
" def dump(self, f):\n",
" joblib.dump([self.V, self.i2w, self.w2i], f)\n",
"\n",
" def cossim(self, w1, w2):\n",
" return cosine_similarity(self[w1], self[w2])[0, 0]\n",
"\n",
" def most_similar(self, positive=[], negative=[], top_n=10):\n",
" v = np.zeros((1, 300), dtype=np.float64)\n",
" for p_word in positive:\n",
" v += self[p_word]\n",
" for n_word in negative:\n",
" v -= self[n_word]\n",
" \n",
" cossims = cosine_similarity(v, self.V)[0]\n",
" if top_n == 1:\n",
" i = cossims.argmax()\n",
" return (self.i2w[i], cossims[i])\n",
" else:\n",
" return [(self.i2w[i], cossims[i])\n",
" for i in cossims.argsort()[-top_n:][::-1]]\n",
"\n",
" @classmethod\n",
" def read_w2v(cls, f):\n",
" \"\"\"Read word2vec output file into Word2Vec\"\"\"\n",
" o = cls()\n",
" o.V = pd.read_table(f, sep=' ', header=None, skiprows=1,\n",
" usecols=range(1, 301)).as_matrix()\n",
" index = pd.read_table(f, sep=' ', header=None, skiprows=1,\n",
" usecols=[0], squeeze=True)\n",
" o.i2w = index.to_dict()\n",
" df = pd.Series(range(len(index)), index=index)\n",
" o.w2i = df.to_dict()\n",
" return o\n",
"\n",
" @classmethod\n",
" def load(cls, f):\n",
" o = cls()\n",
" o.V, o.i2w, o.w2i = joblib.load(f)\n",
" return o"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 90. word2vecによる学習\n",
"81で作成したコーパスに対して[word2vec](https://code.google.com/p/word2vec/)を適用し，単語ベクトルを学習せよ．さらに，学習した単語ベクトルの形式を変換し，86-89のプログラムを動かせ．"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Illegal instruction とかで動かないとき:\n",
"\n",
">There are bugs in older version of GNU GCC, I had the same problem.\n",
">\n",
">My solution was to remove the -march=native switch from the makefile + recompile. Or set it to an older >architecture, like -march=core2 (no SSE4).\n",
">\n",
">Maybe playing with -march could help you too.\n",
"https://groups.google.com/forum/#!topic/word2vec-toolkit/9RvNwbsDCS4\n",
"\n",
"ということで makefile の -march=native を消せば良い．"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Starting training using file ../section9/081.txt\n",
"Vocab size: 221540\n",
"Words in train file: 119733755\n",
"Alpha: 0.000019 Progress: 99.96% Words/thread/sec: 148.73k "
]
}
],
"source": [
"!/home/ryo-t/word2vec/word2vec -train ../section9/081.txt -output vec.txt -size 300 -min-count 10 -threads 20"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"martini01: 4 min 5 s"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting 090.py\n"
]
}
],
"source": [
"%%file 090.py\n",
"#!/usr/bin/env python\n",
"# -*- coding: utf-8 -*-\n",
"\n",
"# http://nbviewer.ipython.org/gist/reiyw/1c8597e0a0a31480bdef\n",
"\n",
"# /home/ryo-t/word2vec/word2vec -train ../section9/081.txt -output vec.txt -size 300 -min-count 10 -threads 20\n",
"\n",
"from section10 import Word2Vec\n",
"\n",
"model2 = Word2Vec.read_w2v('vec.txt')\n",
"model2.dump('090.pkl')\n",
"\n",
"print model2['United_States']\n",
"print\n",
"print model2.cossim('United_States', 'U.S')\n",
"print\n",
"for w, s in model2.most_similar(positive=['England']):\n",
" print '{}\\t{}'.format(w, s)\n",
"print\n",
"for w, s in model2.most_similar(positive=['Spain', 'Athens'],\n",
" negative=['Madrid']):\n",
" print '{}\\t{}'.format(w, s)"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.111715 -1.678478 -0.864159 1.247929 -0.439764 -0.298672 1.268478\n",
" -0.082099 0.080981 0.995544 -0.384532 1.40676 -0.53481 -1.151855\n",
" -0.518142 -1.367582 1.366456 0.234625 -0.516311 -0.095291 -0.748714\n",
" 0.841791 1.496152 0.350041 1.140357 -2.420939 1.352847 -0.173209\n",
" 0.896188 -0.129512 0.546448 0.149908 -0.117161 -0.451165 -0.102921\n",
" 0.653438 0.374661 -1.387798 0.098642 -1.072616 -2.00483 1.907256\n",
" -0.257587 1.121666 -0.828053 2.025138 -1.197662 -1.259046 0.76203\n",
" 1.436384 0.284751 -1.093685 0.682815 0.381928 0.349953 1.142918\n",
" 0.856754 -2.245866 -0.301419 2.441176 -1.203341 -0.61452 1.245822\n",
" -0.098934 0.116288 -0.415464 2.884483 0.327127 1.361002 -0.191948\n",
" 1.011061 -0.37533 0.608045 -0.963722 -0.462832 1.600859 -0.67012\n",
" -0.499299 0.36239 0.073066 -1.274562 1.424533 1.216278 0.687973\n",
" 2.598031 -1.223612 0.291417 1.394731 0.242559 1.755175 0.499459\n",
" 0.491577 -0.919591 0.710396 -0.66756 -0.496358 -0.439576 0.992573\n",
" 0.086781 -0.142619 -0.296617 2.248684 1.292712 0.075918 -0.834879\n",
" -0.382488 -0.839242 -1.048907 -0.237913 0.953505 -0.081171 0.092501\n",
" 0.520527 -0.431623 -1.073667 -0.067161 -0.865561 0.949702 0.533324\n",
" 0.016426 -0.795592 -0.995903 0.456382 -0.370817 -1.903166 -0.409797\n",
" 1.831857 -0.824925 -0.944655 0.32088 0.184281 0.825783 0.468223\n",
" -0.928771 -1.492743 0.859029 0.186948 0.343578 -1.336065 -0.721023\n",
" 1.172585 1.006954 -0.653457 2.305563 2.04682 1.132173 -0.501687\n",
" 0.432549 0.659147 -2.433302 1.019559 -0.036409 0.270775 -0.225971\n",
" -0.268393 0.129928 0.590325 -0.368272 0.936539 0.320352 -0.574198\n",
" 0.53978 0.764107 -0.319089 0.036039 0.579867 0.416124 1.392293\n",
" 0.922503 -0.105388 0.507501 -0.543772 -0.102598 -0.162713 1.706299\n",
" -0.743627 -0.789363 0.070407 -0.324554 1.149525 -0.042674 2.030963\n",
" 1.287475 2.100451 -0.051233 -0.441165 0.013198 1.194631 -0.594926\n",
" 0.723572 0.015485 1.512177 0.011943 -1.439295 -0.597655 1.261545\n",
" 0.313414 0.878825 0.983662 -0.602747 -0.876778 -0.045669 -0.570803\n",
" 0.737866 -0.009308 -1.780431 -0.108762 0.654239 1.064872 -0.908159\n",
" 0.800741 0.733661 -0.546596 -0.966376 -0.016401 -0.590403 1.206174\n",
" -0.264878 -0.101766 0.734613 -0.41348 0.520904 1.178214 2.151731\n",
" -0.117178 0.919975 1.521517 -0.149483 -0.297437 0.347252 -0.140379\n",
" -1.019047 0.438582 -0.609142 -0.814532 0.434932 -0.437515 0.880328\n",
" -1.250725 1.189424 0.567798 0.473766 -1.632663 -1.663404 -0.41828\n",
" 0.573294 -0.974091 0.916487 1.022862 1.044081 -1.445011 0.717363\n",
" -0.14945 0.697323 -0.385095 -0.780732 -0.561399 2.06935 0.663012\n",
" -0.156587 -0.304351 1.20327 0.631807 -1.143068 1.164369 1.305839\n",
" 1.098166 -0.02333 0.705542 1.234169 -1.601554 0.688642 1.005565\n",
" -1.891333 1.540494 1.393429 0.181187 -0.402846 0.763285 -0.796843\n",
" 0.086079 1.348497 -0.256522 0.223945 2.040326 -0.87963 0.950109\n",
" 0.530789 -0.175751 -1.338919 0.221925 -0.588069 0.660469 0.698856\n",
" 0.944344 -0.081147 0.86803 -1.691959 -0.771726 0.742686]\n",
"\n",
"0.841141655387\n",
"\n",
"England\t1.0\n",
"Scotland\t0.660139009332\n",
"Wales\t0.608513301558\n",
"Hampshire\t0.606190210486\n",
"France\t0.597359257096\n",
"England's\t0.589533584488\n",
"Britain\t0.580839969448\n",
"Spain\t0.560133969431\n",
"Ireland\t0.558050197607\n",
"Norcia\t0.52339053709\n",
"\n",
"Athens\t0.710413553166\n",
"Greece\t0.602419197958\n",
"Spain\t0.534001225968\n",
"Crete\t0.497963834225\n",
"Persia\t0.489504547672\n",
"Epirus\t0.484077936871\n",
"Italy\t0.483523274684\n",
"Thessaly\t0.467394545221\n",
"Phoenicia\t0.460826534864\n",
"Rome\t0.451262295106\n",
"\n",
"IPython CPU timings (estimated):\n",
" User : 31.17 s.\n",
" System : 6.57 s.\n",
"Wall time: 35.37 s.\n"
]
}
],
"source": [
"%run -t 090.py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 91. アナロジーデータの準備\n",
"[単語アナロジーの評価データ](https://word2vec.googlecode.com/svn/trunk/questions-words.txt)をダウンロードせよ．このデータ中で\": \"で始まる行はセクション名を表す．例えば，\": capital-common-countries\"という行は，\"capital-common-countries\"というセクションの開始を表している．ダウンロードした評価データの中で，\"family\"というセクションに含まれる評価事例を抜き出してファイルに保存せよ．"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2015-10-02 19:01:22-- https://word2vec.googlecode.com/svn/trunk/questions-words.txt\n",
"word2vec.googlecode.com (word2vec.googlecode.com) をDNSに問いあわせています... 173.194.72.82, 2404:6800:4008:c01::52\n",
"word2vec.googlecode.com (word2vec.googlecode.com)|173.194.72.82|:443 に接続しています... 接続しました。\n",
"HTTP による接続要求を送信しました、応答を待っています... 200 OK\n",
"長さ: 603955 (590K) [text/plain]\n",
"`questions-words.txt' に保存中\n",
"\n",
"100%[======================================>] 603,955 1.04MB/s 時間 0.6s \n",
"\n",
"2015-10-02 19:01:23 (1.04 MB/s) - `questions-words.txt' へ保存完了 [603955/603955]\n",
"\n"
]
}
],
"source": [
"!wget https://word2vec.googlecode.com/svn/trunk/questions-words.txt"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
": capital-common-countries\r\n",
"Athens Greece Baghdad Iraq\r\n",
"Athens Greece Bangkok Thailand\r\n",
"Athens Greece Beijing China\r\n",
"Athens Greece Berlin Germany\r\n",
"Athens Greece Bern Switzerland\r\n",
"Athens Greece Cairo Egypt\r\n",
"Athens Greece Canberra Australia\r\n",
"Athens Greece Hanoi Vietnam\r\n",
"Athens Greece Havana Cuba\r\n"
]
}
],
"source": [
"!head questions-words.txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"awk もしくは sift で:"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"!cat questions-words.txt | gawk '/^: family$/{a=1;next} /^: /{a=0} a==1{print}' > 091.txt"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%bash\n",
"cat questions-words.txt | /home/ryo-t/.go/bin/sift -m ': family\\n(.+?): ' --replace '$1' > 091.txt"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"boy girl brother sister\r\n",
"boy girl brothers sisters\r\n",
"boy girl dad mom\r\n",
"boy girl father mother\r\n",
"boy girl grandfather grandmother\r\n",
"boy girl grandpa grandma\r\n",
"boy girl grandson granddaughter\r\n",
"boy girl groom bride\r\n",
"boy girl he she\r\n",
"boy girl his her\r\n"
]
}
],
"source": [
"!head 091.txt"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting 091.py\n"
]
}
],
"source": [
"%%file 091.py\n",
"# $ cat questions-words.txt | gawk '/^: family$/{a=1;next} /^: /{a=0} a==1{print}' > 091.txt\n",
"# $ cat questions-words.txt | sift -m ': family\\n(.+?): ' --replace '$1' > 091.txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 92. アナロジーデータへの適用\n",
"91で作成した評価データの各事例に対して，vec(2列目の単語) - vec(1列目の単語) + vec(3列目の単語)を計算し，そのベクトルと類似度が最も高い単語と，その類似度を求めよ．求めた単語と類似度は，各事例の末尾に追記せよ．このプログラムを85で作成した単語ベクトル，90で作成した単語ベクトルに対して適用せよ．"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting 092.py\n"
]
}
],
"source": [
"%%file 092.py\n",
"#!/usr/bin/env python\n",
"# -*- coding: utf-8 -*-\n",
"\n",
"import pandas as pd\n",
"from section10 import Word2Vec\n",
"\n",
"df = pd.read_table('091.txt', sep=' ', header=None, names=range(1, 5))\n",
"fnames = [\n",
" '085.pkl',\n",
" '090.pkl',\n",
"]\n",
"for i, fname in enumerate(fnames, start=1):\n",
" model = Word2Vec.load(fname)\n",
" df[['word', 'similarity']] = pd.DataFrame(\n",
" model.most_similar(positive=[row[2], row[3]], negative=[row[1]], top_n=1)\n",
" for row in df.itertuples()\n",
" )\n",
" df.to_csv('092_{}.txt'.format(i), sep=' ', header=False, index=False)"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"IPython CPU timings (estimated):\n",
" User : 866.34 s.\n",
" System : 1501.89 s.\n",
"Wall time: 659.39 s.\n"
]
}
],
"source": [
"%run -t 092.py"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"boy girl brother sister brother 0.982353631947\r\n",
"boy girl brothers sisters brothers 0.9184106979\r\n",
"boy girl dad mom dad 0.779891488123\r\n",
"boy girl father mother father 0.978213334033\r\n",
"boy girl grandfather grandmother grandfather 0.887715506914\r\n",
"boy girl grandpa grandma grand 0.82533578585\r\n",
"boy girl grandson granddaughter grandson 0.923680495004\r\n",
"boy girl groom bride girl 0.721983540279\r\n",
"boy girl he she he 0.99474442177\r\n",
"boy girl his her his 0.997641951251\r\n"
]
}
],
"source": [
"!head 092_1.txt"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"boy girl brother sister brother 0.848358715091\r\n",
"boy girl brothers sisters brothers 0.814974628861\r\n",
"boy girl dad mom dad 0.783146907871\r\n",
"boy girl father mother father 0.84021510985\r\n",
"boy girl grandfather grandmother grandfather 0.820154321029\r\n",
"boy girl grandpa grandma girl 0.459268058786\r\n",
"boy girl grandson granddaughter grandson 0.794706150625\r\n",
"boy girl groom bride groom 0.733814917526\r\n",
"boy girl he she he 0.867922128448\r\n",
"boy girl his her his 0.833072462091\r\n"
]
}
],
"source": [
"!head 092_2.txt"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>word</th>\n",
" <th>similarity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>boy</td>\n",
" <td>girl</td>\n",
" <td>brother</td>\n",
" <td>sister</td>\n",
" <td>girl</td>\n",
" <td>0.823944</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>boy</td>\n",
" <td>girl</td>\n",
" <td>brothers</td>\n",
" <td>sisters</td>\n",
" <td>girl</td>\n",
" <td>0.858264</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>boy</td>\n",
" <td>girl</td>\n",
" <td>dad</td>\n",
" <td>mom</td>\n",
" <td>girl</td>\n",
" <td>0.872110</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>boy</td>\n",
" <td>girl</td>\n",
" <td>father</td>\n",
" <td>mother</td>\n",
" <td>girl</td>\n",
" <td>0.827239</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>boy</td>\n",
" <td>girl</td>\n",
" <td>grandfather</td>\n",
" <td>grandmother</td>\n",
" <td>girl</td>\n",
" <td>0.850719</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>boy</td>\n",
" <td>girl</td>\n",
" <td>grandpa</td>\n",
" <td>grandma</td>\n",
" <td>girl</td>\n",
" <td>0.951974</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>boy</td>\n",
" <td>girl</td>\n",
" <td>grandson</td>\n",
" <td>granddaughter</td>\n",
" <td>girl</td>\n",
" <td>0.869277</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>boy</td>\n",
" <td>girl</td>\n",
" <td>groom</td>\n",
" <td>bride</td>\n",
" <td>girl</td>\n",
" <td>0.892803</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>boy</td>\n",
" <td>girl</td>\n",
" <td>he</td>\n",
" <td>she</td>\n",
" <td>girl</td>\n",
" <td>0.797305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>boy</td>\n",
" <td>girl</td>\n",
" <td>his</td>\n",
" <td>her</td>\n",
" <td>girl</td>\n",
" <td>0.847242</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 1 2 3 4 word similarity\n",
"0 boy girl brother sister girl 0.823944\n",
"1 boy girl brothers sisters girl 0.858264\n",
"2 boy girl dad mom girl 0.872110\n",
"3 boy girl father mother girl 0.827239\n",
"4 boy girl grandfather grandmother girl 0.850719\n",
"5 boy girl grandpa grandma girl 0.951974\n",
"6 boy girl grandson granddaughter girl 0.869277\n",
"7 boy girl groom bride girl 0.892803\n",
"8 boy girl he she girl 0.797305\n",
"9 boy girl his her girl 0.847242"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 93. アナロジータスクの正解率の計算\n",
"92で作ったデータを用い，各モデルのアナロジータスクの正解率を求めよ．"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0632411\r\n"
]
}
],
"source": [
"!cat 092_1.txt | gawk '$4==$5{a+=1} {b+=1} END{print a/b}'"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.120553\r\n"
]
}
],
"source": [
"!cat 092_2.txt | gawk '$4==$5{a+=1} {b+=1} END{print a/b}'"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing 093.py\n"
]
}
],
"source": [
"%%file 093.py\n",
"# $ cat 092_1.txt | gawk '$4==$5{a+=1} {b+=1} END{print a/b}'\n",
"# 0.0632411\n",
"# $ cat 092_2.txt | gawk '$4==$5{a+=1} {b+=1} END{print a/b}'\n",
"# 0.120553"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 94. WordSimilarity-353での類似度計算\n",
"[The WordSimilarity-353 Test Collection](http://www.cs.technion.ac.il/~gabr/resources/data/wordsim353/)の評価データを入力とし，1列目と2列目の単語の類似度を計算し，各行の末尾に類似度の値を追加するプログラムを作成せよ．このプログラムを85で作成した単語ベクトル，90で作成した単語ベクトルに対して適用せよ．"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2015-10-04 20:21:25-- http://www.cs.technion.ac.il/~gabr/resources/data/wordsim353/wordsim353.zip\n",
"Resolving www.cs.technion.ac.il (www.cs.technion.ac.il)... 132.68.32.15\n",
"Connecting to www.cs.technion.ac.il (www.cs.technion.ac.il)|132.68.32.15|:80... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 23257 (23K) [application/zip]\n",
"Saving to: 'wordsim353.zip'\n",
"\n",
"100%[======================================>] 23,257 71.1KB/s in 0.3s \n",
"\n",
"2015-10-04 20:21:27 (71.1 KB/s) - 'wordsim353.zip' saved [23257/23257]\n",
"\n"
]
}
],
"source": [
"!wget http://www.cs.technion.ac.il/~gabr/resources/data/wordsim353/wordsim353.zip"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Archive: wordsim353.zip\n",
" inflating: combined.csv \n",
" inflating: set1.csv \n",
" inflating: set2.csv \n",
" inflating: combined.tab \n",
" inflating: set1.tab \n",
" inflating: set2.tab \n",
" inflating: instructions.txt \n"
]
}
],
"source": [
"!unzip wordsim353.zip"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Word 1,Word 2,Human (mean),1,2,3,4,5,6,7,8,9,10,11,12,13\r",
"\r\n",
"love,sex,6.77,9,6,8,8,7,8,8,4,7,2,6,7,8\r",
"\r\n",
"tiger,cat,7.35,9,7,8,7,8,9,8.5,5,6,9,7,5,7\r",
"\r\n",
"tiger,tiger,10.00,10,10,10,10,10,10,10,10,10,10,10,10,10\r",
"\r\n",
"book,paper,7.46,8,8,7,7,8,9,7,6,7,8,9,4,9\r",
"\r\n",
"computer,keyboard,7.62,8,7,9,9,8,8,7,7,6,8,10,3,9\r",
"\r\n",
"computer,internet,7.58,8,6,9,8,8,8,7.5,7,7,7,9,5,9\r",
"\r\n",
"plane,car,5.77,6,6,7,5,3,6,7,6,6,6,7,3,7\r",
"\r\n",
"train,car,6.31,7,7.5,7.5,5,3,6,7,6,6,6,9,4,8\r",
"\r\n",
"telephone,communication,7.50,7,6.5,8,8,6,8,8,7,5,9,9,8,8\r",
"\r\n"
]
}
],
"source": [
"!head set1.csv"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Word 1,Word 2,Human (mean),1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16\r",
"\r\n",
"energy,secretary,1.81,1,0,4,2,4,5,1,1,1,0,1,1,4,0,2,2\r",
"\r\n",
"secretary,senate,5.06,7,1,7,4,4,7,1,3,4,8,4,5,6,7,6,7\r",
"\r\n",
"energy,laboratory,5.09,7,1,7.5,4,6,7,4,6,1,2,4,3,7,9,6,7\r",
"\r\n",
"computer,laboratory,6.78,8,5,8,7,6,9,6,7,6,7.5,4,5,8,9,7,6\r",
"\r\n",
"weapon,secret,6.06,7,4,8,6,6,9,2,6,3,6,5,6,8,9,7,5\r",
"\r\n",
"FBI,fingerprint,6.94,8,6,8,5,5,9,7,7,6,6,6,8,9,7,6,8\r",
"\r\n",
"FBI,investigation,8.31,9,9,8.5,9,7,9,8,8,8,7.5,6,9,10,9,7,9\r",
"\r\n",
"investigation,effort,4.59,5,1,7.5,2,4,7,6,5,2,2,2,7,6,5,6,6\r",
"\r\n",
"Mars,water,2.94,2,1,3,2,1,8,0,4,2,6,1,1,3,0,5,8\r",
"\r\n"
]
}
],
"source": [
"!head set2.csv"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting 094.py\n"
]
}
],
"source": [
"%%file 094.py\n",
"#!/usr/bin/env python\n",
"# -*- coding: utf-8 -*-\n",
"\n",
"import pandas as pd\n",
"from section10 import Word2Vec\n",
"\n",
"df = pd.read_csv('set1.csv')\n",
"fnames = [\n",
" '085.pkl',\n",
" '090.pkl',\n",
"]\n",
"for i, fname in enumerate(fnames, 1):\n",
" model = Word2Vec.load(fname)\n",
" df['Similarity'] = df['Word 1'].combine(df['Word 2'], model.cossim)\n",
" df.to_csv('094_{}.csv'.format(i), index=False)\n",
"\n",
"# Word 1,Word 2,Human (mean),1,2,3,4,5,6,7,8,9,10,11,12,13,Similarity\n",
"# love,sex,6.77,9.0,6.0,8.0,8,7,8,8.0,4,7.0,2,6.0,7,8,0.36839036375870099\n",
"# tiger,cat,7.35,9.0,7.0,8.0,7,8,9,8.5,5,6.0,9,7.0,5,7,0.40347865913854736\n",
"# tiger,tiger,10.0,10.0,10.0,10.0,10,10,10,10.0,10,10.0,10,10.0,10,10,1.0000000000000009\n",
"# book,paper,7.46,8.0,8.0,7.0,7,8,9,7.0,6,7.0,8,9.0,4,9,0.50995876696932663"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"IPython CPU timings (estimated):\n",
" User : 12.90 s.\n",
" System : 1.03 s.\n",
"Wall time: 13.98 s.\n"
]
}
],
"source": [
"%run -t 094.py"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Word 1,Word 2,Human (mean),1,2,3,4,5,6,7,8,9,10,11,12,13,Similarity\r\n",
"love,sex,6.77,9.0,6.0,8.0,8,7,8,8.0,4,7.0,2,6.0,7,8,0.36839036375870099\r\n",
"tiger,cat,7.35,9.0,7.0,8.0,7,8,9,8.5,5,6.0,9,7.0,5,7,0.40347865913854736\r\n",
"tiger,tiger,10.0,10.0,10.0,10.0,10,10,10,10.0,10,10.0,10,10.0,10,10,1.0000000000000009\r\n",
"book,paper,7.46,8.0,8.0,7.0,7,8,9,7.0,6,7.0,8,9.0,4,9,0.50995876696932663\r\n",
"computer,keyboard,7.62,8.0,7.0,9.0,9,8,8,7.0,7,6.0,8,10.0,3,9,0.27786940199370064\r\n",
"computer,internet,7.58,8.0,6.0,9.0,8,8,8,7.5,7,7.0,7,9.0,5,9,0.55358491015606259\r\n",
"plane,car,5.77,6.0,6.0,7.0,5,3,6,7.0,6,6.0,6,7.0,3,7,0.37906096411285894\r\n",
"train,car,6.31,7.0,7.5,7.5,5,3,6,7.0,6,6.0,6,9.0,4,8,0.51998968702416193\r\n",
"telephone,communication,7.5,7.0,6.5,8.0,8,6,8,8.0,7,5.0,9,9.0,8,8,0.7160214943201918\r\n"
]
}
],
"source": [
"!head 094_1.csv"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Word 1,Word 2,Human (mean),1,2,3,4,5,6,7,8,9,10,11,12,13,Similarity\r\n",
"love,sex,6.77,9.0,6.0,8.0,8,7,8,8.0,4,7.0,2,6.0,7,8,0.31620491092471387\r\n",
"tiger,cat,7.35,9.0,7.0,8.0,7,8,9,8.5,5,6.0,9,7.0,5,7,0.52794169357487364\r\n",
"tiger,tiger,10.0,10.0,10.0,10.0,10,10,10,10.0,10,10.0,10,10.0,10,10,1.0000000000000002\r\n",
"book,paper,7.46,8.0,8.0,7.0,7,8,9,7.0,6,7.0,8,9.0,4,9,0.44566080006156927\r\n",
"computer,keyboard,7.62,8.0,7.0,9.0,9,8,8,7.0,7,6.0,8,10.0,3,9,0.44757038642915198\r\n",
"computer,internet,7.58,8.0,6.0,9.0,8,8,8,7.5,7,7.0,7,9.0,5,9,0.40579794682677728\r\n",
"plane,car,5.77,6.0,6.0,7.0,5,3,6,7.0,6,6.0,6,7.0,3,7,0.43123815597401616\r\n",
"train,car,6.31,7.0,7.5,7.5,5,3,6,7.0,6,6.0,6,9.0,4,8,0.47172378213094107\r\n",
"telephone,communication,7.5,7.0,6.5,8.0,8,6,8,8.0,7,5.0,9,9.0,8,8,0.3537576002458982\r\n"
]
}
],
"source": [
"!head 094_2.csv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 95. WordSimilarity-353での評価\n",
"94で作ったデータを用い，各モデルが出力する類似度のランキングと，人間の類似度判定のランキングの間のスピアマン相関係数を計算せよ．"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting 095.py\n"
]
}
],
"source": [
"%%file 095.py\n",
"#!/usr/bin/env python\n",
"# -*- coding: utf-8 -*-\n",
"\n",
"import pandas as pd\n",
"from section10 import Word2Vec\n",
"\n",
"for i in [1, 2]:\n",
" df = pd.read_csv('094_{}.csv'.format(i))\n",
" print 'model{}: {}'.format(\n",
" i, df['Human (mean)'].corr(df['Similarity'], method='spearman'))\n",
"\n",
"# model1: 0.566733040797\n",
"# model2: 0.639301059779"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"model1: 0.566733040797\n",
"model2: 0.639301059779\n",
"\n",
"IPython CPU timings (estimated):\n",
" User : 0.00 s.\n",
" System : 0.01 s.\n",
"Wall time: 0.02 s.\n"
]
}
],
"source": [
"%run -t 095.py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 96. 国名に関するベクトルの抽出\n",
"word2vecの学習結果から，国名に関するベクトルのみを抜き出せ．"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting 096.py\n"
]
}
],
"source": [
"%%file 096.py\n",
"#!/usr/bin/env python\n",
"# -*- coding: utf-8 -*-\n",
"from iso3166 import countries\n",
"\n",
"\n",
"greps = [u'grep \"^{} \" vec.txt &'.format('_'.join(w.strip('.,!?;:()[]\\'\"') \n",
" for w in c[0].split(' ')))\n",
" for c in countries]\n",
"print u'time {} {} wait; {}'.format('{', ' '.join(greps[::-1]), '}')\n",
"\n",
"# $ time { grep \"^Zimbabwe \" vec.txt & grep \"^Zambia \" vec.txt & grep \"^Yemen \" vec.txt & grep \"^Western_Sahara \" vec.txt & grep \"^Wallis_and_Futuna \" vec.txt & grep \"^Virgin_Islands_U.S \" vec.txt & grep \"^Virgin_Islands_British \" vec.txt & grep \"^Viet_Nam \" vec.txt & grep \"^Venezuela_Bolivarian_Republic_of \" vec.txt & grep \"^Vanuatu \" vec.txt & grep \"^Uzbekistan \" vec.txt & grep \"^Uruguay \" vec.txt & grep \"^United_States_Minor_Outlying_Islands \" vec.txt & grep \"^United_States \" vec.txt & grep \"^United_Kingdom \" vec.txt & grep \"^United_Arab_Emirates \" vec.txt & grep \"^Ukraine \" vec.txt & grep \"^Uganda \" vec.txt & grep \"^Tuvalu \" vec.txt & grep \"^Turks_and_Caicos_Islands \" vec.txt & grep \"^Turkmenistan \" vec.txt & grep \"^Turkey \" vec.txt & grep \"^Tunisia \" vec.txt & grep \"^Trinidad_and_Tobago \" vec.txt & grep \"^Tonga \" vec.txt & grep \"^Tokelau \" vec.txt & grep \"^Togo \" vec.txt & grep \"^Timor-Leste \" vec.txt & grep \"^Thailand \" vec.txt & grep \"^Tanzania_United_Republic_of \" vec.txt & grep \"^Tajikistan \" vec.txt & grep \"^Taiwan_Province_of_China \" vec.txt & grep \"^Syrian_Arab_Republic \" vec.txt & grep \"^Switzerland \" vec.txt & grep \"^Sweden \" vec.txt & grep \"^Swaziland \" vec.txt & grep \"^Svalbard_and_Jan_Mayen \" vec.txt & grep \"^Suriname \" vec.txt & grep \"^Sudan \" vec.txt & grep \"^Sri_Lanka \" vec.txt & grep \"^Spain \" vec.txt & grep \"^South_Sudan \" vec.txt & grep \"^South_Georgia_and_the_South_Sandwich_Islands \" vec.txt & grep \"^South_Africa \" vec.txt & grep \"^Somalia \" vec.txt & grep \"^Solomon_Islands \" vec.txt & grep \"^Slovenia \" vec.txt & grep \"^Slovakia \" vec.txt & grep \"^Sint_Maarten_Dutch_part \" vec.txt & grep \"^Singapore \" vec.txt & grep \"^Sierra_Leone \" vec.txt & grep \"^Seychelles \" vec.txt & grep \"^Serbia \" vec.txt & grep \"^Senegal \" vec.txt & grep \"^Saudi_Arabia \" vec.txt & grep \"^Sao_Tome_and_Principe \" vec.txt & grep \"^San_Marino \" vec.txt & grep \"^Samoa \" vec.txt & grep \"^Saint_Vincent_and_the_Grenadines \" vec.txt & grep \"^Saint_Pierre_and_Miquelon \" vec.txt & grep \"^Saint_Martin_French_part \" vec.txt & grep \"^Saint_Lucia \" vec.txt & grep \"^Saint_Kitts_and_Nevis \" vec.txt & grep \"^Saint_Helena_Ascension_and_Tristan_da_Cunha \" vec.txt & grep \"^Saint_Barthélemy \" vec.txt & grep \"^Rwanda \" vec.txt & grep \"^Russian_Federation \" vec.txt & grep \"^Romania \" vec.txt & grep \"^Réunion \" vec.txt & grep \"^Qatar \" vec.txt & grep \"^Puerto_Rico \" vec.txt & grep \"^Portugal \" vec.txt & grep \"^Poland \" vec.txt & grep \"^Pitcairn \" vec.txt & grep \"^Philippines \" vec.txt & grep \"^Peru \" vec.txt & grep \"^Paraguay \" vec.txt & grep \"^Papua_New_Guinea \" vec.txt & grep \"^Panama \" vec.txt & grep \"^Palestine_State_of \" vec.txt & grep \"^Palau \" vec.txt & grep \"^Pakistan \" vec.txt & grep \"^Oman \" vec.txt & grep \"^Norway \" vec.txt & grep \"^Northern_Mariana_Islands \" vec.txt & grep \"^Norfolk_Island \" vec.txt & grep \"^Niue \" vec.txt & grep \"^Nigeria \" vec.txt & grep \"^Niger \" vec.txt & grep \"^Nicaragua \" vec.txt & grep \"^New_Zealand \" vec.txt & grep \"^New_Caledonia \" vec.txt & grep \"^Netherlands \" vec.txt & grep \"^Nepal \" vec.txt & grep \"^Nauru \" vec.txt & grep \"^Namibia \" vec.txt & grep \"^Myanmar \" vec.txt & grep \"^Mozambique \" vec.txt & grep \"^Morocco \" vec.txt & grep \"^Montserrat \" vec.txt & grep \"^Montenegro \" vec.txt & grep \"^Mongolia \" vec.txt & grep \"^Monaco \" vec.txt & grep \"^Moldova_Republic_of \" vec.txt & grep \"^Micronesia_Federated_States_of \" vec.txt & grep \"^Mexico \" vec.txt & grep \"^Mayotte \" vec.txt & grep \"^Mauritius \" vec.txt & grep \"^Mauritania \" vec.txt & grep \"^Martinique \" vec.txt & grep \"^Marshall_Islands \" vec.txt & grep \"^Malta \" vec.txt & grep \"^Mali \" vec.txt & grep \"^Maldives \" vec.txt & grep \"^Malaysia \" vec.txt & grep \"^Malawi \" vec.txt & grep \"^Madagascar \" vec.txt & grep \"^Macedonia_the_former_Yugoslav_Republic_of \" vec.txt & grep \"^Macao \" vec.txt & grep \"^Luxembourg \" vec.txt & grep \"^Lithuania \" vec.txt & grep \"^Liechtenstein \" vec.txt & grep \"^Libya \" vec.txt & grep \"^Liberia \" vec.txt & grep \"^Lesotho \" vec.txt & grep \"^Lebanon \" vec.txt & grep \"^Latvia \" vec.txt & grep \"^Lao_People's_Democratic_Republic \" vec.txt & grep \"^Kyrgyzstan \" vec.txt & grep \"^Kuwait \" vec.txt & grep \"^Korea_Republic_of \" vec.txt & grep \"^Korea_Democratic_People's_Republic_of \" vec.txt & grep \"^Kiribati \" vec.txt & grep \"^Kenya \" vec.txt & grep \"^Kazakhstan \" vec.txt & grep \"^Jordan \" vec.txt & grep \"^Jersey \" vec.txt & grep \"^Japan \" vec.txt & grep \"^Jamaica \" vec.txt & grep \"^Italy \" vec.txt & grep \"^Israel \" vec.txt & grep \"^Isle_of_Man \" vec.txt & grep \"^Ireland \" vec.txt & grep \"^Iraq \" vec.txt & grep \"^Iran_Islamic_Republic_of \" vec.txt & grep \"^Indonesia \" vec.txt & grep \"^India \" vec.txt & grep \"^Iceland \" vec.txt & grep \"^Hungary \" vec.txt & grep \"^Hong_Kong \" vec.txt & grep \"^Honduras \" vec.txt & grep \"^Holy_See \" vec.txt & grep \"^Heard_Island_and_McDonald_Islands \" vec.txt & grep \"^Haiti \" vec.txt & grep \"^Guyana \" vec.txt & grep \"^Guinea-Bissau \" vec.txt & grep \"^Guinea \" vec.txt & grep \"^Guernsey \" vec.txt & grep \"^Guatemala \" vec.txt & grep \"^Guam \" vec.txt & grep \"^Guadeloupe \" vec.txt & grep \"^Grenada \" vec.txt & grep \"^Greenland \" vec.txt & grep \"^Greece \" vec.txt & grep \"^Gibraltar \" vec.txt & grep \"^Ghana \" vec.txt & grep \"^Germany \" vec.txt & grep \"^Georgia \" vec.txt & grep \"^Gambia \" vec.txt & grep \"^Gabon \" vec.txt & grep \"^French_Southern_Territories \" vec.txt & grep \"^French_Polynesia \" vec.txt & grep \"^French_Guiana \" vec.txt & grep \"^France \" vec.txt & grep \"^Finland \" vec.txt & grep \"^Fiji \" vec.txt & grep \"^Faroe_Islands \" vec.txt & grep \"^Falkland_Islands_Malvinas \" vec.txt & grep \"^Ethiopia \" vec.txt & grep \"^Estonia \" vec.txt & grep \"^Eritrea \" vec.txt & grep \"^Equatorial_Guinea \" vec.txt & grep \"^El_Salvador \" vec.txt & grep \"^Egypt \" vec.txt & grep \"^Ecuador \" vec.txt & grep \"^Dominican_Republic \" vec.txt & grep \"^Dominica \" vec.txt & grep \"^Djibouti \" vec.txt & grep \"^Denmark \" vec.txt & grep \"^Czech_Republic \" vec.txt & grep \"^Cyprus \" vec.txt & grep \"^Curaçao \" vec.txt & grep \"^Cuba \" vec.txt & grep \"^Croatia \" vec.txt & grep \"^Côte_d'Ivoire \" vec.txt & grep \"^Costa_Rica \" vec.txt & grep \"^Cook_Islands \" vec.txt & grep \"^Congo_Democratic_Republic_of_the \" vec.txt & grep \"^Congo \" vec.txt & grep \"^Comoros \" vec.txt & grep \"^Colombia \" vec.txt & grep \"^Cocos_Keeling_Islands \" vec.txt & grep \"^Christmas_Island \" vec.txt & grep \"^China \" vec.txt & grep \"^Chile \" vec.txt & grep \"^Chad \" vec.txt & grep \"^Central_African_Republic \" vec.txt & grep \"^Cayman_Islands \" vec.txt & grep \"^Cabo_Verde \" vec.txt & grep \"^Canada \" vec.txt & grep \"^Cameroon \" vec.txt & grep \"^Cambodia \" vec.txt & grep \"^Burundi \" vec.txt & grep \"^Burkina_Faso \" vec.txt & grep \"^Bulgaria \" vec.txt & grep \"^Brunei_Darussalam \" vec.txt & grep \"^British_Indian_Ocean_Territory \" vec.txt & grep \"^Brazil \" vec.txt & grep \"^Bouvet_Island \" vec.txt & grep \"^Botswana \" vec.txt & grep \"^Bosnia_and_Herzegovina \" vec.txt & grep \"^Bonaire_Sint_Eustatius_and_Saba \" vec.txt & grep \"^Bolivia_Plurinational_State_of \" vec.txt & grep \"^Bhutan \" vec.txt & grep \"^Bermuda \" vec.txt & grep \"^Benin \" vec.txt & grep \"^Belize \" vec.txt & grep \"^Belgium \" vec.txt & grep \"^Belarus \" vec.txt & grep \"^Barbados \" vec.txt & grep \"^Bangladesh \" vec.txt & grep \"^Bahrain \" vec.txt & grep \"^Bahamas \" vec.txt & grep \"^Azerbaijan \" vec.txt & grep \"^Austria \" vec.txt & grep \"^Australia \" vec.txt & grep \"^Aruba \" vec.txt & grep \"^Armenia \" vec.txt & grep \"^Argentina \" vec.txt & grep \"^Antigua_and_Barbuda \" vec.txt & grep \"^Antarctica \" vec.txt & grep \"^Anguilla \" vec.txt & grep \"^Angola \" vec.txt & grep \"^Andorra \" vec.txt & grep \"^American_Samoa \" vec.txt & grep \"^Algeria \" vec.txt & grep \"^Albania \" vec.txt & grep \"^Åland_Islands \" vec.txt & grep \"^Afghanistan \" vec.txt & wait; } > vec_country.txt\n",
"# real\t0m22.211s\n",
"# user\t1m20.969s\n",
"# sys\t3m2.300s"
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"time { grep \"^Zimbabwe \" vec.txt & grep \"^Zambia \" vec.txt & grep \"^Yemen \" vec.txt & grep \"^Western_Sahara \" vec.txt & grep \"^Wallis_and_Futuna \" vec.txt & grep \"^Virgin_Islands_U.S \" vec.txt & grep \"^Virgin_Islands_British \" vec.txt & grep \"^Viet_Nam \" vec.txt & grep \"^Venezuela_Bolivarian_Republic_of \" vec.txt & grep \"^Vanuatu \" vec.txt & grep \"^Uzbekistan \" vec.txt & grep \"^Uruguay \" vec.txt & grep \"^United_States_Minor_Outlying_Islands \" vec.txt & grep \"^United_States \" vec.txt & grep \"^United_Kingdom \" vec.txt & grep \"^United_Arab_Emirates \" vec.txt & grep \"^Ukraine \" vec.txt & grep \"^Uganda \" vec.txt & grep \"^Tuvalu \" vec.txt & grep \"^Turks_and_Caicos_Islands \" vec.txt & grep \"^Turkmenistan \" vec.txt & grep \"^Turkey \" vec.txt & grep \"^Tunisia \" vec.txt & grep \"^Trinidad_and_Tobago \" vec.txt & grep \"^Tonga \" vec.txt & grep \"^Tokelau \" vec.txt & grep \"^Togo \" vec.txt & grep \"^Timor-Leste \" vec.txt & grep \"^Thailand \" vec.txt & grep \"^Tanzania_United_Republic_of \" vec.txt & grep \"^Tajikistan \" vec.txt & grep \"^Taiwan_Province_of_China \" vec.txt & grep \"^Syrian_Arab_Republic \" vec.txt & grep \"^Switzerland \" vec.txt & grep \"^Sweden \" vec.txt & grep \"^Swaziland \" vec.txt & grep \"^Svalbard_and_Jan_Mayen \" vec.txt & grep \"^Suriname \" vec.txt & grep \"^Sudan \" vec.txt & grep \"^Sri_Lanka \" vec.txt & grep \"^Spain \" vec.txt & grep \"^South_Sudan \" vec.txt & grep \"^South_Georgia_and_the_South_Sandwich_Islands \" vec.txt & grep \"^South_Africa \" vec.txt & grep \"^Somalia \" vec.txt & grep \"^Solomon_Islands \" vec.txt & grep \"^Slovenia \" vec.txt & grep \"^Slovakia \" vec.txt & grep \"^Sint_Maarten_Dutch_part \" vec.txt & grep \"^Singapore \" vec.txt & grep \"^Sierra_Leone \" vec.txt & grep \"^Seychelles \" vec.txt & grep \"^Serbia \" vec.txt & grep \"^Senegal \" vec.txt & grep \"^Saudi_Arabia \" vec.txt & grep \"^Sao_Tome_and_Principe \" vec.txt & grep \"^San_Marino \" vec.txt & grep \"^Samoa \" vec.txt & grep \"^Saint_Vincent_and_the_Grenadines \" vec.txt & grep \"^Saint_Pierre_and_Miquelon \" vec.txt & grep \"^Saint_Martin_French_part \" vec.txt & grep \"^Saint_Lucia \" vec.txt & grep \"^Saint_Kitts_and_Nevis \" vec.txt & grep \"^Saint_Helena_Ascension_and_Tristan_da_Cunha \" vec.txt & grep \"^Saint_Barthélemy \" vec.txt & grep \"^Rwanda \" vec.txt & grep \"^Russian_Federation \" vec.txt & grep \"^Romania \" vec.txt & grep \"^Réunion \" vec.txt & grep \"^Qatar \" vec.txt & grep \"^Puerto_Rico \" vec.txt & grep \"^Portugal \" vec.txt & grep \"^Poland \" vec.txt & grep \"^Pitcairn \" vec.txt & grep \"^Philippines \" vec.txt & grep \"^Peru \" vec.txt & grep \"^Paraguay \" vec.txt & grep \"^Papua_New_Guinea \" vec.txt & grep \"^Panama \" vec.txt & grep \"^Palestine_State_of \" vec.txt & grep \"^Palau \" vec.txt & grep \"^Pakistan \" vec.txt & grep \"^Oman \" vec.txt & grep \"^Norway \" vec.txt & grep \"^Northern_Mariana_Islands \" vec.txt & grep \"^Norfolk_Island \" vec.txt & grep \"^Niue \" vec.txt & grep \"^Nigeria \" vec.txt & grep \"^Niger \" vec.txt & grep \"^Nicaragua \" vec.txt & grep \"^New_Zealand \" vec.txt & grep \"^New_Caledonia \" vec.txt & grep \"^Netherlands \" vec.txt & grep \"^Nepal \" vec.txt & grep \"^Nauru \" vec.txt & grep \"^Namibia \" vec.txt & grep \"^Myanmar \" vec.txt & grep \"^Mozambique \" vec.txt & grep \"^Morocco \" vec.txt & grep \"^Montserrat \" vec.txt & grep \"^Montenegro \" vec.txt & grep \"^Mongolia \" vec.txt & grep \"^Monaco \" vec.txt & grep \"^Moldova_Republic_of \" vec.txt & grep \"^Micronesia_Federated_States_of \" vec.txt & grep \"^Mexico \" vec.txt & grep \"^Mayotte \" vec.txt & grep \"^Mauritius \" vec.txt & grep \"^Mauritania \" vec.txt & grep \"^Martinique \" vec.txt & grep \"^Marshall_Islands \" vec.txt & grep \"^Malta \" vec.txt & grep \"^Mali \" vec.txt & grep \"^Maldives \" vec.txt & grep \"^Malaysia \" vec.txt & grep \"^Malawi \" vec.txt & grep \"^Madagascar \" vec.txt & grep \"^Macedonia_the_former_Yugoslav_Republic_of \" vec.txt & grep \"^Macao \" vec.txt & grep \"^Luxembourg \" vec.txt & grep \"^Lithuania \" vec.txt & grep \"^Liechtenstein \" vec.txt & grep \"^Libya \" vec.txt & grep \"^Liberia \" vec.txt & grep \"^Lesotho \" vec.txt & grep \"^Lebanon \" vec.txt & grep \"^Latvia \" vec.txt & grep \"^Lao_People's_Democratic_Republic \" vec.txt & grep \"^Kyrgyzstan \" vec.txt & grep \"^Kuwait \" vec.txt & grep \"^Korea_Republic_of \" vec.txt & grep \"^Korea_Democratic_People's_Republic_of \" vec.txt & grep \"^Kiribati \" vec.txt & grep \"^Kenya \" vec.txt & grep \"^Kazakhstan \" vec.txt & grep \"^Jordan \" vec.txt & grep \"^Jersey \" vec.txt & grep \"^Japan \" vec.txt & grep \"^Jamaica \" vec.txt & grep \"^Italy \" vec.txt & grep \"^Israel \" vec.txt & grep \"^Isle_of_Man \" vec.txt & grep \"^Ireland \" vec.txt & grep \"^Iraq \" vec.txt & grep \"^Iran_Islamic_Republic_of \" vec.txt & grep \"^Indonesia \" vec.txt & grep \"^India \" vec.txt & grep \"^Iceland \" vec.txt & grep \"^Hungary \" vec.txt & grep \"^Hong_Kong \" vec.txt & grep \"^Honduras \" vec.txt & grep \"^Holy_See \" vec.txt & grep \"^Heard_Island_and_McDonald_Islands \" vec.txt & grep \"^Haiti \" vec.txt & grep \"^Guyana \" vec.txt & grep \"^Guinea-Bissau \" vec.txt & grep \"^Guinea \" vec.txt & grep \"^Guernsey \" vec.txt & grep \"^Guatemala \" vec.txt & grep \"^Guam \" vec.txt & grep \"^Guadeloupe \" vec.txt & grep \"^Grenada \" vec.txt & grep \"^Greenland \" vec.txt & grep \"^Greece \" vec.txt & grep \"^Gibraltar \" vec.txt & grep \"^Ghana \" vec.txt & grep \"^Germany \" vec.txt & grep \"^Georgia \" vec.txt & grep \"^Gambia \" vec.txt & grep \"^Gabon \" vec.txt & grep \"^French_Southern_Territories \" vec.txt & grep \"^French_Polynesia \" vec.txt & grep \"^French_Guiana \" vec.txt & grep \"^France \" vec.txt & grep \"^Finland \" vec.txt & grep \"^Fiji \" vec.txt & grep \"^Faroe_Islands \" vec.txt & grep \"^Falkland_Islands_Malvinas \" vec.txt & grep \"^Ethiopia \" vec.txt & grep \"^Estonia \" vec.txt & grep \"^Eritrea \" vec.txt & grep \"^Equatorial_Guinea \" vec.txt & grep \"^El_Salvador \" vec.txt & grep \"^Egypt \" vec.txt & grep \"^Ecuador \" vec.txt & grep \"^Dominican_Republic \" vec.txt & grep \"^Dominica \" vec.txt & grep \"^Djibouti \" vec.txt & grep \"^Denmark \" vec.txt & grep \"^Czech_Republic \" vec.txt & grep \"^Cyprus \" vec.txt & grep \"^Curaçao \" vec.txt & grep \"^Cuba \" vec.txt & grep \"^Croatia \" vec.txt & grep \"^Côte_d'Ivoire \" vec.txt & grep \"^Costa_Rica \" vec.txt & grep \"^Cook_Islands \" vec.txt & grep \"^Congo_Democratic_Republic_of_the \" vec.txt & grep \"^Congo \" vec.txt & grep \"^Comoros \" vec.txt & grep \"^Colombia \" vec.txt & grep \"^Cocos_Keeling_Islands \" vec.txt & grep \"^Christmas_Island \" vec.txt & grep \"^China \" vec.txt & grep \"^Chile \" vec.txt & grep \"^Chad \" vec.txt & grep \"^Central_African_Republic \" vec.txt & grep \"^Cayman_Islands \" vec.txt & grep \"^Cabo_Verde \" vec.txt & grep \"^Canada \" vec.txt & grep \"^Cameroon \" vec.txt & grep \"^Cambodia \" vec.txt & grep \"^Burundi \" vec.txt & grep \"^Burkina_Faso \" vec.txt & grep \"^Bulgaria \" vec.txt & grep \"^Brunei_Darussalam \" vec.txt & grep \"^British_Indian_Ocean_Territory \" vec.txt & grep \"^Brazil \" vec.txt & grep \"^Bouvet_Island \" vec.txt & grep \"^Botswana \" vec.txt & grep \"^Bosnia_and_Herzegovina \" vec.txt & grep \"^Bonaire_Sint_Eustatius_and_Saba \" vec.txt & grep \"^Bolivia_Plurinational_State_of \" vec.txt & grep \"^Bhutan \" vec.txt & grep \"^Bermuda \" vec.txt & grep \"^Benin \" vec.txt & grep \"^Belize \" vec.txt & grep \"^Belgium \" vec.txt & grep \"^Belarus \" vec.txt & grep \"^Barbados \" vec.txt & grep \"^Bangladesh \" vec.txt & grep \"^Bahrain \" vec.txt & grep \"^Bahamas \" vec.txt & grep \"^Azerbaijan \" vec.txt & grep \"^Austria \" vec.txt & grep \"^Australia \" vec.txt & grep \"^Aruba \" vec.txt & grep \"^Armenia \" vec.txt & grep \"^Argentina \" vec.txt & grep \"^Antigua_and_Barbuda \" vec.txt & grep \"^Antarctica \" vec.txt & grep \"^Anguilla \" vec.txt & grep \"^Angola \" vec.txt & grep \"^Andorra \" vec.txt & grep \"^American_Samoa \" vec.txt & grep \"^Algeria \" vec.txt & grep \"^Albania \" vec.txt & grep \"^Åland_Islands \" vec.txt & grep \"^Afghanistan \" vec.txt & wait; }\n"
]
}
],
"source": [
"%run 096.py"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"real\t0m22.211s\n",
"user\t1m20.969s\n",
"sys\t3m2.300s\n"
]
}
],
"source": [
"%%bash\n",
"time { grep \"^Zimbabwe \" vec.txt & grep \"^Zambia \" vec.txt & grep \"^Yemen \" vec.txt & grep \"^Western_Sahara \" vec.txt & grep \"^Wallis_and_Futuna \" vec.txt & grep \"^Virgin_Islands_U.S \" vec.txt & grep \"^Virgin_Islands_British \" vec.txt & grep \"^Viet_Nam \" vec.txt & grep \"^Venezuela_Bolivarian_Republic_of \" vec.txt & grep \"^Vanuatu \" vec.txt & grep \"^Uzbekistan \" vec.txt & grep \"^Uruguay \" vec.txt & grep \"^United_States_Minor_Outlying_Islands \" vec.txt & grep \"^United_States \" vec.txt & grep \"^United_Kingdom \" vec.txt & grep \"^United_Arab_Emirates \" vec.txt & grep \"^Ukraine \" vec.txt & grep \"^Uganda \" vec.txt & grep \"^Tuvalu \" vec.txt & grep \"^Turks_and_Caicos_Islands \" vec.txt & grep \"^Turkmenistan \" vec.txt & grep \"^Turkey \" vec.txt & grep \"^Tunisia \" vec.txt & grep \"^Trinidad_and_Tobago \" vec.txt & grep \"^Tonga \" vec.txt & grep \"^Tokelau \" vec.txt & grep \"^Togo \" vec.txt & grep \"^Timor-Leste \" vec.txt & grep \"^Thailand \" vec.txt & grep \"^Tanzania_United_Republic_of \" vec.txt & grep \"^Tajikistan \" vec.txt & grep \"^Taiwan_Province_of_China \" vec.txt & grep \"^Syrian_Arab_Republic \" vec.txt & grep \"^Switzerland \" vec.txt & grep \"^Sweden \" vec.txt & grep \"^Swaziland \" vec.txt & grep \"^Svalbard_and_Jan_Mayen \" vec.txt & grep \"^Suriname \" vec.txt & grep \"^Sudan \" vec.txt & grep \"^Sri_Lanka \" vec.txt & grep \"^Spain \" vec.txt & grep \"^South_Sudan \" vec.txt & grep \"^South_Georgia_and_the_South_Sandwich_Islands \" vec.txt & grep \"^South_Africa \" vec.txt & grep \"^Somalia \" vec.txt & grep \"^Solomon_Islands \" vec.txt & grep \"^Slovenia \" vec.txt & grep \"^Slovakia \" vec.txt & grep \"^Sint_Maarten_Dutch_part \" vec.txt & grep \"^Singapore \" vec.txt & grep \"^Sierra_Leone \" vec.txt & grep \"^Seychelles \" vec.txt & grep \"^Serbia \" vec.txt & grep \"^Senegal \" vec.txt & grep \"^Saudi_Arabia \" vec.txt & grep \"^Sao_Tome_and_Principe \" vec.txt & grep \"^San_Marino \" vec.txt & grep \"^Samoa \" vec.txt & grep \"^Saint_Vincent_and_the_Grenadines \" vec.txt & grep \"^Saint_Pierre_and_Miquelon \" vec.txt & grep \"^Saint_Martin_French_part \" vec.txt & grep \"^Saint_Lucia \" vec.txt & grep \"^Saint_Kitts_and_Nevis \" vec.txt & grep \"^Saint_Helena_Ascension_and_Tristan_da_Cunha \" vec.txt & grep \"^Saint_Barthélemy \" vec.txt & grep \"^Rwanda \" vec.txt & grep \"^Russian_Federation \" vec.txt & grep \"^Romania \" vec.txt & grep \"^Réunion \" vec.txt & grep \"^Qatar \" vec.txt & grep \"^Puerto_Rico \" vec.txt & grep \"^Portugal \" vec.txt & grep \"^Poland \" vec.txt & grep \"^Pitcairn \" vec.txt & grep \"^Philippines \" vec.txt & grep \"^Peru \" vec.txt & grep \"^Paraguay \" vec.txt & grep \"^Papua_New_Guinea \" vec.txt & grep \"^Panama \" vec.txt & grep \"^Palestine_State_of \" vec.txt & grep \"^Palau \" vec.txt & grep \"^Pakistan \" vec.txt & grep \"^Oman \" vec.txt & grep \"^Norway \" vec.txt & grep \"^Northern_Mariana_Islands \" vec.txt & grep \"^Norfolk_Island \" vec.txt & grep \"^Niue \" vec.txt & grep \"^Nigeria \" vec.txt & grep \"^Niger \" vec.txt & grep \"^Nicaragua \" vec.txt & grep \"^New_Zealand \" vec.txt & grep \"^New_Caledonia \" vec.txt & grep \"^Netherlands \" vec.txt & grep \"^Nepal \" vec.txt & grep \"^Nauru \" vec.txt & grep \"^Namibia \" vec.txt & grep \"^Myanmar \" vec.txt & grep \"^Mozambique \" vec.txt & grep \"^Morocco \" vec.txt & grep \"^Montserrat \" vec.txt & grep \"^Montenegro \" vec.txt & grep \"^Mongolia \" vec.txt & grep \"^Monaco \" vec.txt & grep \"^Moldova_Republic_of \" vec.txt & grep \"^Micronesia_Federated_States_of \" vec.txt & grep \"^Mexico \" vec.txt & grep \"^Mayotte \" vec.txt & grep \"^Mauritius \" vec.txt & grep \"^Mauritania \" vec.txt & grep \"^Martinique \" vec.txt & grep \"^Marshall_Islands \" vec.txt & grep \"^Malta \" vec.txt & grep \"^Mali \" vec.txt & grep \"^Maldives \" vec.txt & grep \"^Malaysia \" vec.txt & grep \"^Malawi \" vec.txt & grep \"^Madagascar \" vec.txt & grep \"^Macedonia_the_former_Yugoslav_Republic_of \" vec.txt & grep \"^Macao \" vec.txt & grep \"^Luxembourg \" vec.txt & grep \"^Lithuania \" vec.txt & grep \"^Liechtenstein \" vec.txt & grep \"^Libya \" vec.txt & grep \"^Liberia \" vec.txt & grep \"^Lesotho \" vec.txt & grep \"^Lebanon \" vec.txt & grep \"^Latvia \" vec.txt & grep \"^Lao_People's_Democratic_Republic \" vec.txt & grep \"^Kyrgyzstan \" vec.txt & grep \"^Kuwait \" vec.txt & grep \"^Korea_Republic_of \" vec.txt & grep \"^Korea_Democratic_People's_Republic_of \" vec.txt & grep \"^Kiribati \" vec.txt & grep \"^Kenya \" vec.txt & grep \"^Kazakhstan \" vec.txt & grep \"^Jordan \" vec.txt & grep \"^Jersey \" vec.txt & grep \"^Japan \" vec.txt & grep \"^Jamaica \" vec.txt & grep \"^Italy \" vec.txt & grep \"^Israel \" vec.txt & grep \"^Isle_of_Man \" vec.txt & grep \"^Ireland \" vec.txt & grep \"^Iraq \" vec.txt & grep \"^Iran_Islamic_Republic_of \" vec.txt & grep \"^Indonesia \" vec.txt & grep \"^India \" vec.txt & grep \"^Iceland \" vec.txt & grep \"^Hungary \" vec.txt & grep \"^Hong_Kong \" vec.txt & grep \"^Honduras \" vec.txt & grep \"^Holy_See \" vec.txt & grep \"^Heard_Island_and_McDonald_Islands \" vec.txt & grep \"^Haiti \" vec.txt & grep \"^Guyana \" vec.txt & grep \"^Guinea-Bissau \" vec.txt & grep \"^Guinea \" vec.txt & grep \"^Guernsey \" vec.txt & grep \"^Guatemala \" vec.txt & grep \"^Guam \" vec.txt & grep \"^Guadeloupe \" vec.txt & grep \"^Grenada \" vec.txt & grep \"^Greenland \" vec.txt & grep \"^Greece \" vec.txt & grep \"^Gibraltar \" vec.txt & grep \"^Ghana \" vec.txt & grep \"^Germany \" vec.txt & grep \"^Georgia \" vec.txt & grep \"^Gambia \" vec.txt & grep \"^Gabon \" vec.txt & grep \"^French_Southern_Territories \" vec.txt & grep \"^French_Polynesia \" vec.txt & grep \"^French_Guiana \" vec.txt & grep \"^France \" vec.txt & grep \"^Finland \" vec.txt & grep \"^Fiji \" vec.txt & grep \"^Faroe_Islands \" vec.txt & grep \"^Falkland_Islands_Malvinas \" vec.txt & grep \"^Ethiopia \" vec.txt & grep \"^Estonia \" vec.txt & grep \"^Eritrea \" vec.txt & grep \"^Equatorial_Guinea \" vec.txt & grep \"^El_Salvador \" vec.txt & grep \"^Egypt \" vec.txt & grep \"^Ecuador \" vec.txt & grep \"^Dominican_Republic \" vec.txt & grep \"^Dominica \" vec.txt & grep \"^Djibouti \" vec.txt & grep \"^Denmark \" vec.txt & grep \"^Czech_Republic \" vec.txt & grep \"^Cyprus \" vec.txt & grep \"^Curaçao \" vec.txt & grep \"^Cuba \" vec.txt & grep \"^Croatia \" vec.txt & grep \"^Côte_d'Ivoire \" vec.txt & grep \"^Costa_Rica \" vec.txt & grep \"^Cook_Islands \" vec.txt & grep \"^Congo_Democratic_Republic_of_the \" vec.txt & grep \"^Congo \" vec.txt & grep \"^Comoros \" vec.txt & grep \"^Colombia \" vec.txt & grep \"^Cocos_Keeling_Islands \" vec.txt & grep \"^Christmas_Island \" vec.txt & grep \"^China \" vec.txt & grep \"^Chile \" vec.txt & grep \"^Chad \" vec.txt & grep \"^Central_African_Republic \" vec.txt & grep \"^Cayman_Islands \" vec.txt & grep \"^Cabo_Verde \" vec.txt & grep \"^Canada \" vec.txt & grep \"^Cameroon \" vec.txt & grep \"^Cambodia \" vec.txt & grep \"^Burundi \" vec.txt & grep \"^Burkina_Faso \" vec.txt & grep \"^Bulgaria \" vec.txt & grep \"^Brunei_Darussalam \" vec.txt & grep \"^British_Indian_Ocean_Territory \" vec.txt & grep \"^Brazil \" vec.txt & grep \"^Bouvet_Island \" vec.txt & grep \"^Botswana \" vec.txt & grep \"^Bosnia_and_Herzegovina \" vec.txt & grep \"^Bonaire_Sint_Eustatius_and_Saba \" vec.txt & grep \"^Bolivia_Plurinational_State_of \" vec.txt & grep \"^Bhutan \" vec.txt & grep \"^Bermuda \" vec.txt & grep \"^Benin \" vec.txt & grep \"^Belize \" vec.txt & grep \"^Belgium \" vec.txt & grep \"^Belarus \" vec.txt & grep \"^Barbados \" vec.txt & grep \"^Bangladesh \" vec.txt & grep \"^Bahrain \" vec.txt & grep \"^Bahamas \" vec.txt & grep \"^Azerbaijan \" vec.txt & grep \"^Austria \" vec.txt & grep \"^Australia \" vec.txt & grep \"^Aruba \" vec.txt & grep \"^Armenia \" vec.txt & grep \"^Argentina \" vec.txt & grep \"^Antigua_and_Barbuda \" vec.txt & grep \"^Antarctica \" vec.txt & grep \"^Anguilla \" vec.txt & grep \"^Angola \" vec.txt & grep \"^Andorra \" vec.txt & grep \"^American_Samoa \" vec.txt & grep \"^Algeria \" vec.txt & grep \"^Albania \" vec.txt & grep \"^Åland_Islands \" vec.txt & grep \"^Afghanistan \" vec.txt & wait; } > vec_country.txt"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wallis_and_Futuna 0.127953 -0.262669\r\n",
"French_Polynesia -0.157886 -0.223186\r\n",
"Trinidad_and_Tobago 0.341821 -0.919370\r\n",
"United_Kingdom 0.263154 -1.304215\r\n",
"Solomon_Islands 0.663484 0.284290\r\n",
"Norfolk_Island 0.567501 0.358714\r\n",
"Russian_Federation 1.466403 -3.079000\r\n",
"Antigua_and_Barbuda 0.017221 -0.505327\r\n",
"Dominican_Republic 0.345445 -0.756788\r\n",
"Svalbard_and_Jan_Mayen -0.084691 -0.122349\r\n"
]
}
],
"source": [
"!head vec_country.txt | cut -d ' ' -f 1-3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 97. k-meansクラスタリング\n",
"96の単語ベクトルに対して，k-meansクラスタリングをクラスタ数$k=5$として実行せよ．"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting 097.py\n"
]
}
],
"source": [
"%%file 097.py\n",
"#!/usr/bin/env python\n",
"# -*- coding: utf-8 -*-\n",
"import pandas as pd\n",
"from sklearn.cluster import KMeans\n",
"\n",
"from section10 import Word2Vec\n",
"\n",
"model = Word2Vec.read_w2v('vec_country.txt')\n",
"est = KMeans(n_clusters=5, random_state=0)\n",
"cls = pd.DataFrame(est.fit_predict(model.V), columns=['class'])\n",
"for i, group in enumerate(cls.groupby(['class']).groups.itervalues()):\n",
" print '{}:'.format(i)\n",
" print ' '.join(model.i2w[i] for i in group)\n",
"\n",
"# 0:\n",
"# United_Kingdom Russian_Federation Netherlands Germany Bosnia_and_Herzegovina Belgium Slovenia Czech_Republic Sweden Albania Iceland Croatia Finland Switzerland Romania Lithuania Poland France Norway Slovakia Italy Serbia Luxembourg Ireland Greece Austria Montenegro Portugal Ukraine Hungary Estonia\n",
"# 1:\n",
"# Uzbekistan Turkmenistan United_Arab_Emirates Azerbaijan Malaysia Pakistan Tunisia Myanmar Cyprus Qatar Afghanistan Bahrain Tajikistan Sri_Lanka Saudi_Arabia India Kazakhstan Kyrgyzstan Egypt Israel Mongolia Indonesia Libya Bangladesh Thailand Kuwait Oman Armenia Singapore Cambodia Turkey Yemen Lebanon Bhutan Algeria China Nepal Iraq\n",
"# 2:\n",
"# Dominican_Republic Philippines Argentina Guatemala United_States Paraguay Uruguay Colombia Jersey Panama Haiti Japan El_Salvador Peru Costa_Rica Nicaragua Mexico Guam Brazil Chile Cuba\n",
"# 3:\n",
"# Uganda Sierra_Leone Mauritania Zimbabwe South_Africa Mozambique Zambia Senegal Ethiopia Rwanda South_Sudan Namibia Guinea Burkina_Faso Mali Gabon Angola Botswana Ghana Congo Nigeria Somalia Eritrea Liberia Cameroon Kenya Burundi Niger Malawi\n",
"# 4:\n",
"# French_Polynesia Trinidad_and_Tobago Solomon_Islands Norfolk_Island Antigua_and_Barbuda Svalbard_and_Jan_Mayen Northern_Mariana_Islands Western_Sahara Saint_Kitts_and_Nevis French_Guiana San_Marino Turks_and_Caicos_Islands Antarctica British_Indian_Ocean_Territory Saint_Lucia Saint_Pierre_and_Miquelon Saint_Vincent_and_the_Grenadines Equatorial_Guinea Cook_Islands Hong_Kong Swaziland Syrian_Arab_Republic American_Samoa New_Caledonia Cocos_Keeling_Islands Australia Central_African_Republic Christmas_Island Pitcairn Vanuatu New_Zealand Papua_New_Guinea Anguilla Gibraltar Cayman_Islands Maldives Holy_See Barbados Grenada Jamaica Timor-Leste Bahamas Andorra Marshall_Islands Lesotho Liechtenstein Mauritius Bermuda Guinea-Bissau Gambia Canada Isle_of_Man Niue Madagascar Fiji Cabo_Verde Togo Faroe_Islands Djibouti Malta Nauru Georgia Suriname Montserrat Seychelles Tuvalu Aruba Dominica Chad Kiribati Martinique Palau Greenland Samoa Tonga Mayotte Jordan Belize Guyana Comoros Monaco Macao"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0:\n",
"United_Kingdom Russian_Federation Netherlands Germany Bosnia_and_Herzegovina Belgium Slovenia Czech_Republic Sweden Albania Iceland Croatia Finland Switzerland Romania Lithuania Poland France Norway Slovakia Italy Serbia Luxembourg Ireland Greece Austria Montenegro Portugal Ukraine Hungary Estonia\n",
"1:\n",
"Uzbekistan Turkmenistan United_Arab_Emirates Azerbaijan Malaysia Pakistan Tunisia Myanmar Cyprus Qatar Afghanistan Bahrain Tajikistan Sri_Lanka Saudi_Arabia India Kazakhstan Kyrgyzstan Egypt Israel Mongolia Indonesia Libya Bangladesh Thailand Kuwait Oman Armenia Singapore Cambodia Turkey Yemen Lebanon Bhutan Algeria China Nepal Iraq\n",
"2:\n",
"Dominican_Republic Philippines Argentina Guatemala United_States Paraguay Uruguay Colombia Jersey Panama Haiti Japan El_Salvador Peru Costa_Rica Nicaragua Mexico Guam Brazil Chile Cuba\n",
"3:\n",
"Uganda Sierra_Leone Mauritania Zimbabwe South_Africa Mozambique Zambia Senegal Ethiopia Rwanda South_Sudan Namibia Guinea Burkina_Faso Mali Gabon Angola Botswana Ghana Congo Nigeria Somalia Eritrea Liberia Cameroon Kenya Burundi Niger Malawi\n",
"4:\n",
"French_Polynesia Trinidad_and_Tobago Solomon_Islands Norfolk_Island Antigua_and_Barbuda Svalbard_and_Jan_Mayen Northern_Mariana_Islands Western_Sahara Saint_Kitts_and_Nevis French_Guiana San_Marino Turks_and_Caicos_Islands Antarctica British_Indian_Ocean_Territory Saint_Lucia Saint_Pierre_and_Miquelon Saint_Vincent_and_the_Grenadines Equatorial_Guinea Cook_Islands Hong_Kong Swaziland Syrian_Arab_Republic American_Samoa New_Caledonia Cocos_Keeling_Islands Australia Central_African_Republic Christmas_Island Pitcairn Vanuatu New_Zealand Papua_New_Guinea Anguilla Gibraltar Cayman_Islands Maldives Holy_See Barbados Grenada Jamaica Timor-Leste Bahamas Andorra Marshall_Islands Lesotho Liechtenstein Mauritius Bermuda Guinea-Bissau Gambia Canada Isle_of_Man Niue Madagascar Fiji Cabo_Verde Togo Faroe_Islands Djibouti Malta Nauru Georgia Suriname Montserrat Seychelles Tuvalu Aruba Dominica Chad Kiribati Martinique Palau Greenland Samoa Tonga Mayotte Jordan Belize Guyana Comoros Monaco Macao\n",
"\n",
"IPython CPU timings (estimated):\n",
" User : 0.28 s.\n",
" System : 1.34 s.\n",
"Wall time: 0.18 s.\n"
]
}
],
"source": [
"%run -t 097.py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 98. Ward法によるクラスタリング\n",
"96の単語ベクトルに対して，Ward法による階層型クラスタリングを実行せよ．さらに，クラスタリング結果をデンドログラムとして可視化せよ．"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- [SciPy Hierarchical Clustering and Dendrogram Tutorial](https://joernhees.de/blog/2015/08/26/scipy-hierarchical-clustering-and-dendrogram-tutorial/)"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting 098.py\n"
]
}
],
"source": [
"%%file 098.py\n",
"#!/usr/bin/env python\n",
"# -*- coding: utf-8 -*-\n",
"\n",
"import matplotlib.pyplot as plt\n",
"from scipy.cluster.hierarchy import dendrogram, linkage\n",
"from sklearn.cluster import KMeans\n",
"\n",
"from section10 import Word2Vec\n",
"\n",
"model = Word2Vec.read_w2v('vec_country.txt')\n",
"Z = linkage(model.V, 'ward')\n",
"plt.figure(figsize=(30, 15))\n",
"plt.title('Hierarchical Clustering Dendrogram')\n",
"plt.xlabel('word')\n",
"plt.ylabel('distance')\n",
"dendrogram(\n",
" Z,\n",
" leaf_rotation=90., # rotates the x axis labels\n",
" leaf_font_size=8., # font size for the x axis labels\n",
" labels=model.get_rows(),\n",
")\n",
"plt.savefig('/home/ryo-t/public_html/nlp100/098.pdf',\n",
" bbox_inches='tight', frameon=False)"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"IPython CPU timings (estimated):\n",
" User : 2.44 s.\n",
" System : 0.90 s.\n",
"Wall time: 2.53 s.\n"
]
},
{
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lnWNTLM6MQIHSMbw1zHufzdgksdZa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gofsGgP98JWkVpCO4pW0t+undu+JIhCVX2GWEvsiSKymhZaNlX1iyJyLYbwOw0rTsGAgvXQc2/E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K4F\nfgqcDzxubBRJC+7HbX8MkMQCgBbBF4H3MdmO8OgkALR9/chQ0kgWtiRJ0pplAUuSpOWVZBfgBuBc\n4GbgRuAvR2aStPCuS3LqdHzN0CRaBC8ZHQB4BvaplH6BPbYkSdKal+Ro4DQmjaYvbXvU4EiSJC2E\n6cXlE5lcXP4k8AHgIW2fPTSYpIWWZC+AtpeOziJJmj1XbEmSJMFdgacDJwCPGpxFkqRFclPbjUmO\nAU5se0aSt40OJWlxJXkucAiwQ5K2PWh0JknSbFnYkiRJggCPbPuCJH84OowkSQtkQ5KXAgcBJyVZ\nD+w2OJOkxfZg4OC2t44OIklaHm5FKEmSJEmSlkWSOwP7A+e3vSbJnsCuba8YHE3SgkpyLPDmtteP\nziJJWh4WtiRJ0pqV5J1tn5/k7M2m3a5EkiRJmlNJPgbsDNwC0PbAsYkkSbNmYUuSJK15SfZo+53p\neEPb747OJEmSJEmSpF+2bnQASZKkUZKsS3J34Mgke0zHJ4zOJUmSJGnrJDl1+nz2Zo9PjM4lSZo9\nV2xJkqQ1K8n2wN8BDwK+DBT4UtuThwaTJEmStFWS3KntD0fnkCQtPwtbkiRpzUtyt7bXjs4hSZIk\naWmSnAlcA7yr7QWj80iSlo+FLUmStGYlObXti5Kcvdl02x40LJQkSZKkJUlyT+BPgIcBXwDe0/ab\nY1NJkmbNwpYkSVqzkuzc9qdJ1re9ZnQeSZIkSdsuyd7AccB92z5sdB5J0mxZ2JIkSWtekne3PWJ0\nDkmSJElLk2RP4DDgACb9c9/V9ktjU0mSlsP2owNIkiStAndLcjFwGZOtCJ82OpAkSZKkrXIa8F7g\niW1v3jSZZNe2142LJUmatXWjA0iSJI2SZH2SI4EPAPsBNwJ/PzaVJEmSpK3V9qltP7J5UWvq0CGB\nJEnLxsKWJElay04GrgR2BE4HXgQ8Y2giSZIkSZIk/UpuRShJktayq9qeBZMG022vTnL16FCSJEmS\nJEm6fRa2JEnSWvbQJMcAAR6Y5Fhg38GZJEmSJM3Oj0cHkCTNVtqOziBJkjREkkcDv3Qy1PaClU8j\nSZIkaVsleXHbt0zHr2x7wuhMkqTZsrAlSZIkSZIkaa4l2Ql4P7AXsHE6/f22hw0LJUlaFha2JEmS\nJEmSJC2EJPu0vWh0DknS8rHHliRJkiRJkqRFsSHJm4AdgLY9anQgSdJsWdiSJEmSJEmStCieCZwA\n3Do6iCRpeVjYkiRJkiRJkrQoLmv71dEhJEnLxx5bkiRJkiRJkhZCkvOZ3Mz/I4C2Bw4NJEmaOQtb\nkiRJkiRJkiRJmgtuRShJkiRJkiRpriV5ddvXJjlts+m2PWpYKEnSsnDFliRJkiRJkqS5luS+bTcm\nuS/wswuebb82LJQkaVm4YkuSJEmSJEnSXGu7cTrcHnjC9Hk9cNyoTJKk5bFudABJkiRJkiRJmpGX\nA/cBLgZuGJxFkrQMLGxJkiRJkiRJWhTXAtcB5wO7j40iSVoOFrYkSZIkSZIkzbUkuyRZB5wLnAfc\nyKTAJUlaMBa2JEmSJEmSJM27k4B7A58HXge8DNhraCJJ0rKwsCVJkiRJkiRp3t3UdiNwJHBi21OA\n68dGkiQtBwtbkiRJkiRJkubdhiQvBQ4CPpZkPbDb4EySpGWQtqMzSJIkSZIkSdKSJbkzsD9wfttr\nkuwJ7Nr2isHRJEkzZmFLkiRJkiRJkiRJc8GtCCVJkiRJkiRJkjQXLGxJkiRJkiRJkiRpLljYkiRJ\nkiRJkiRJ0lywsCVJkiRJcyjJEUleNzqHJEmSJK0kC1uSJEmSJEmSJEmaCxa2JEmSJGkFJPlskntP\nxxcmOWQ6PjnJUUnOSXJeknOT7DX93buTvCDJ2Zk4OskXkpwBPHLgP0eSJEmShrCwJUmSJEkr43Tg\nKUnuBVwJPH06/wjgWcBftX0M8Erg3dPfFdiz7YHAA4DDgH3bHgzcsoLZJUmSJGlVsLAlSZIkSSvj\nA8AfA4cCbwfukmRf4IvAb7b9d4C2nwfus9nrzpo+/w5wYdtNBa3PrUhqSZIkSVpFLGxJkiRJ0gpo\n+13gRuCxwHnAh4FTmKzkuiLJQwGSPBj4xmYvvWn6fDHwB0l2TLIOOGClskuSJEnSarH96ACSJEmS\ntIZ8EHhI21uTfBA4tu0FSZ4HnJpkB+BW4DmbvaYAbb+S5F1MVmp9D/jPTb+TJEmSpLUird+DJEmS\nJEmSJEmStPq5FaEkSZIkSZIkSZLmgoUtSZIkSZIkSZIkzQULW5IkSZIkSZIkSZoLFrYkSZIkSZIk\nSZI0FyxsSZIkSZIkSZIkaS5Y2JIkSZIkSZIkSdJcsLAlSZIkSZIkSZKkuWBhS5IkSZIkSZIkSXPh\n/wDK4XET/FZ7RgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f75dd9e6690>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%run -t 098.py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 99. t-SNEによる可視化\n",
"96の単語ベクトルに対して，ベクトル空間をt-SNEで可視化せよ．"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting 099.py\n"
]
}
],
"source": [
"%%file 099.py\n",
"#!/usr/bin/env python\n",
"# -*- coding: utf-8 -*-\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from sklearn.cluster import KMeans\n",
"from sklearn.manifold import TSNE\n",
"\n",
"from section10 import Word2Vec\n",
"\n",
"# k-means\n",
"model = Word2Vec.read_w2v('vec_country.txt')\n",
"est = KMeans(n_clusters=5, random_state=0)\n",
"cls = est.fit_predict(model.V)\n",
"\n",
"# t-SNE\n",
"ts = TSNE(n_components=2, random_state=0)\n",
"V_new = ts.fit_transform(model.V)\n",
"\n",
"colors = ['c', 'b', 'r', 'y', 'm']\n",
"plt.figure(figsize=(15, 15))\n",
"plt.xlim(V_new[:, 0].min() - 10, V_new[:, 0].max() + 10)\n",
"plt.ylim(V_new[:, 1].min() - 10, V_new[:, 1].max() + 10)\n",
"for i, c in enumerate(cls):\n",
" x, y = V_new[i]\n",
" plt.text(x, y, model.i2w[i], color=colors[c])\n",
"plt.savefig('/home/ryo-t/public_html/nlp100/099.pdf',\n",
" bbox_inches='tight', frameon=False)"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"IPython CPU timings (estimated):\n",
" User : 2.16 s.\n",
" System : 3.02 s.\n",
"Wall time: 2.00 s.\n"
]
},
{
"data": {
"image/png": 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vkAk1Cj8rXClPpyg4KrgD3xE0oIUPAAAAcIHEmYkqerpIklS8sFhJNyY1rqva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JEvkmE09Mn06Dy8Xv6+vRSBLLw8PHjefd7m7uamhALUlcEB3NjYmJFNtsPN7aikaSaHC7mRcS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\nIlFEKuh6qYumB5rw9YiKqwFngM7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PKGgRBP+An8TrEklYnkDZvDL8g2II43DlMIYS0aMl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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f75d297c950>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%run -t 099.py"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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