Notebook_0_1_Read_Google_news embeddings.ipynb

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   "execution_count": 1,
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   "source": [
    "import csv\n",
    "import re\n",
    "from collections import Counter\n",
    "import itertools\n",
    "\n",
    "urlFinder = re.compile('\\w+:\\/{2}[\\d\\w-]+(\\.[\\d\\w-]+)*(?:(?:\\/[^\\s/]*))*')\n",
    "atNameFinder = re.compile(r'@([A-Za-z0-9_]+)')\n",
    "atNameCounter = 0\n",
    "\n",
    "exclude_punc = set([\n",
    "        \"!\",\n",
    "        \"?\",\n",
    "        \".\",\n",
    "        \",\",\n",
    "        \":\",\n",
    "        \";\",\n",
    "        \"'\",\n",
    "        \"\\\"\",\n",
    "        \"“\",\n",
    "        \"’\",\n",
    "        \"-\"\n",
    "])\n",
    "\n",
    "\n",
    "sentences = []\n",
    "labels = []\n",
    "x = []\n",
    "y = []\n",
    "_y = []\n",
    "\n",
    "with open('data.csv', 'rb') as f:\n",
    "    reader = csv.reader(f, delimiter=',')   \n",
    "    \n",
    "    for row in reader:\n",
    "        words = []\n",
    "        \n",
    "        for word in row[1] \\\n",
    "            .strip() \\\n",
    "            .replace(\"&amp;\", \"\") \\\n",
    "            .replace(\"&gt;\",\"\") \\\n",
    "            .replace(\"&lt;\", \"\") \\\n",
    "            .lower().split():\n",
    "            \n",
    "            if urlFinder.match(word):\n",
    "                words.append(\"<URL/>\")\n",
    "            elif atNameFinder.search(word):\n",
    "                words.append(\"<AT_NAME/>\")\n",
    "                atNameCounter +=1\n",
    "            else:\n",
    "                word = ''.join(ch for ch in word if ch not in exclude_punc)\n",
    "                words.append(word)\n",
    "        sentences.append(words)\n",
    "        labels.append(([0, 1] if row[0] == \"example\" else [1, 0]))\n",
    "        _y.append(1 if row[0] == \"example\" else 0)\n",
    "\n",
    "sequence_length = max(len(i) for i in sentences)\n",
    "padded_sentences = []\n",
    "for i in range(len(sentences)):\n",
    "    sentence = sentences[i]\n",
    "    num_padding = sequence_length - len(sentence)\n",
    "    new_sentence = sentence + [\"<PAD/>\"] * num_padding\n",
    "    padded_sentences.append(new_sentence)        \n",
    "        \n",
    "word_counts = Counter(itertools.chain(*padded_sentences))\n",
    "\n",
    "# Mapping from index to word\n",
    "vocabulary_inv = [x[0] for x in word_counts.most_common()]\n",
    "# Mapping from word to index\n",
    "vocabulary = {x: i for i, x in enumerate(vocabulary_inv)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "word_vecs = {}\n",
    "with open(\"GoogleNews-vectors-negative300.bin\", \"rb\") as f:\n",
    "    header = f.readline()\n",
    "    vocab_size, layer1_size = map(int, header.split())\n",
    "    binary_len = np.dtype('float32').itemsize * layer1_size\n",
    "    for line in xrange(vocab_size):\n",
    "        word = []\n",
    "        while True:\n",
    "            ch = f.read(1)\n",
    "            if ch == ' ':\n",
    "                word = ''.join(word)\n",
    "                break\n",
    "            if ch != '\\n':\n",
    "                word.append(ch)   \n",
    "        if word in vocabulary:\n",
    "            word_vecs[word] = np.fromstring(f.read(binary_len), dtype='float32')  \n",
    "        else:\n",
    "            f.read(binary_len)\n",
    "            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "four\n",
      "hanging\n",
      "woody\n",
      "towns\n",
      "looking\n",
      "eligible\n",
      "impoverishment\n",
      "lord\n",
      "sinking\n",
      "navigating\n"
     ]
    }
   ],
   "source": [
    "for key, ct in zip(word_vecs, xrange(10)):\n",
    "    print(key)"
   ]
  }
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