Eng_FB_Link_Posts_Headline_Skip_Thought.ipynb

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   "source": [
    "import Utils.FS.file as file\n",
    "import TextPreprocess.cleaner as cleaner\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "import Utils.misc as helper\n",
    "import Utils.pandas_helper as ph\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "deletable": true,
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   "source": [
    "import TextEmbedding.skipthoughts as skipthoughts\n",
    "model = skipthoughts.load_model()\n",
    "encoder = skipthoughts.Encoder(model)"
   ]
  },
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   "cell_type": "code",
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   "metadata": {
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   "source": [
    "eng_link_posts = file.read('eng_link_posts_2.pkl')\n",
    "eng_link_posts['name'] = eng_link_posts['name'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "eng_link_posts['name'].to_csv('./headline.txt', header=False, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
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   "outputs": [],
   "source": [
    "print(eng_link_posts.shape)\n",
    "eng_link_posts[['id', 'name', 'message', 'description']].sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def chunker(seq, size):\n",
    "    return (seq[pos:pos + size] for pos in range(0, len(seq), size))\n",
    "\n",
    "vectors = np.array([]).reshape(0, 4800)\n",
    "\n",
    "j = 0;\n",
    "for i in chunker(eng_link_posts['name'].sample(200000), 1000000):\n",
    "    j = j + 100000;\n",
    "    print('processing up to {} headlines'.format(j))\n",
    "    vectors = np.vstack([vectors, encoder.encode(i, verbose=False)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": [
    "def closest_headline(line):\n",
    "    vec = encoder.encode([line], verbose=False)\n",
    "    dist = np.sum((vectors - vec)**2, axis=1)\n",
    "    return eng_link_posts['name'][dist.argsort()[0:10]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
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   "outputs": [],
   "source": [
    "ans = closest_headline(\"The 10 Best Nonfiction Books Of All Time You Should Not Miss\")\n",
    "ans.apply(print)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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