Doc2Vec.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Basic Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import gensim\n",
    "import os\n",
    "import logging\n",
    "\n",
    "# Log settings\n",
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Training - Import file list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "docList = []\n",
    "docList = [f for f in listdir(\"D:\\\\COCA & GloWbE\\\\COCA\\\\Pre-processed Text\\\\Science-related Texts\\\\\") if f.endswith('.txt')]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10258"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(docList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Note that this imports ALL FILES in dir\n",
    "class SentenceIterator(object):\n",
    "\n",
    "    def __init__(self, dirname):\n",
    "        self.dirname = dirname\n",
    "\n",
    "    def __iter__(self):\n",
    "        for fname in os.listdir(self.dirname):\n",
    "            for line in open(os.path.join(self.dirname, fname)):\n",
    "                yield gensim.utils.simple_preprocess(line)\n",
    "\n",
    "# a memory - friendly iterator\n",
    "sentence_interator = SentenceIterator('D:\\\\COCA & GloWbE\\\\COCA\\\\Pre-processed Text\\\\Science-related Texts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get doc labels\n",
    "- Doc labels are *titles* of each text\n",
    "- titles are at the forefront of a text, with the delimiters %&% front to back on the title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "directory = \"D:\\\\COCA & GloWbE\\\\COCA\\\\Pre-processed Text\\\\Science-related Texts\\\\\"\n",
    "pattern = re.compile(r\"%&%.*%&%\") # to get title of text\n",
    "\n",
    "titleList = []\n",
    "\n",
    "for fileName in docList:\n",
    "    \n",
    "    with open(directory + fileName, encoding='utf-8') as f:\n",
    "        text = f.read()\n",
    "    \n",
    "    matched = pattern.match(text)\n",
    "    title = matched.group().strip(\"%&%\")\n",
    "    titleList.append(title)\n",
    "    \n",
    "    # print(title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load file data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "texts = []\n",
    "\n",
    "for file in os.listdir(directory):\n",
    "    texts.append( str( open( os.path.join(directory, file), encoding='utf-8').readlines() ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Labeled Sentence interator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import gensim\n",
    "# LabeledSentence = gensim.models.doc2vec.LabeledSentence\n",
    "# preprocess = gensim.utils.simple_preprocess\n",
    "\n",
    "\n",
    "class LabeledLineSentence(object):\n",
    "    def __init__(self, doc_list, labels_list):\n",
    "        LabeledSentence = gensim.models.doc2vec.LabeledSentence\n",
    "        preprocess = gensim.utils.simple_preprocess\n",
    "        self.labels_list = labels_list\n",
    "        self.doc_list = doc_list\n",
    "    def __iter__(self):\n",
    "        for idx, doc in enumerate(self.doc_list):\n",
    "            yield LabeledSentence(words=preprocess(doc),tags=[self.labels_list[idx]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sentences = LabeledLineSentence(texts, titleList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-11-04 21:52:49,322 : INFO : collecting all words and their counts\n",
      "2017-11-04 21:52:49,327 : INFO : PROGRESS: at example #0, processed 0 words (0/s), 0 word types, 0 tags\n",
      "2017-11-04 21:53:30,102 : INFO : PROGRESS: at example #10000, processed 23403653 words (573990/s), 199240 word types, 9206 tags\n",
      "2017-11-04 21:53:30,589 : INFO : collected 199240 word types and 9206 unique tags from a corpus of 10258 examples and 23683065 words\n",
      "2017-11-04 21:53:30,590 : INFO : Loading a fresh vocabulary\n",
      "2017-11-04 21:53:31,731 : INFO : min_count=5 retains 72438 unique words (36% of original 199240, drops 126802)\n",
      "2017-11-04 21:53:31,732 : INFO : min_count=5 leaves 23475162 word corpus (99% of original 23683065, drops 207903)\n",
      "2017-11-04 21:53:32,046 : INFO : deleting the raw counts dictionary of 199240 items\n",
      "2017-11-04 21:53:32,054 : INFO : sample=0.001 downsamples 32 most-common words\n",
      "2017-11-04 21:53:32,056 : INFO : downsampling leaves estimated 18884695 word corpus (80.4% of prior 23475162)\n",
      "2017-11-04 21:53:32,057 : INFO : estimated required memory for 72438 words and 300 dimensions: 222958600 bytes\n",
      "2017-11-04 21:53:32,343 : INFO : resetting layer weights\n",
      "2017-11-04 21:53:33,611 : INFO : training model with 11 workers on 72438 vocabulary and 300 features, using sg=0 hs=0 sample=0.001 negative=5 window=10\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-11-04 21:58:44,604 : INFO : PROGRESS: at 90.37% examples, 280525 words/s, in_qsize 20, out_qsize 1\n",
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      "2017-11-04 21:59:08,094 : INFO : PROGRESS: at 99.24% examples, 279973 words/s, in_qsize 21, out_qsize 0\n",
      "2017-11-04 21:59:08,971 : INFO : worker thread finished; awaiting finish of 10 more threads\n",
      "2017-11-04 21:59:08,978 : INFO : worker thread finished; awaiting finish of 9 more threads\n",
      "2017-11-04 21:59:08,989 : INFO : worker thread finished; awaiting finish of 8 more threads\n",
      "2017-11-04 21:59:09,007 : INFO : worker thread finished; awaiting finish of 7 more threads\n",
      "2017-11-04 21:59:09,013 : INFO : worker thread finished; awaiting finish of 6 more threads\n",
      "2017-11-04 21:59:09,019 : INFO : worker thread finished; awaiting finish of 5 more threads\n",
      "2017-11-04 21:59:09,029 : INFO : worker thread finished; awaiting finish of 4 more threads\n",
      "2017-11-04 21:59:09,037 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2017-11-04 21:59:09,055 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2017-11-04 21:59:09,059 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2017-11-04 21:59:09,081 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2017-11-04 21:59:09,083 : INFO : training on 118415325 raw words (94038777 effective words) took 335.5s, 280327 effective words/s\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'it' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-113-f6577a7a96cc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0malpha\u001b[0m \u001b[1;33m-=\u001b[0m \u001b[1;36m0.002\u001b[0m \u001b[1;31m# decrease the learning rate\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin_alpha\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0malpha\u001b[0m \u001b[1;31m# fix the learning rate, no deca\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mit\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtotal_examples\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcorpus_count\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miter\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'it' is not defined"
     ]
    }
   ],
   "source": [
    "# This is too slow to run\n",
    "model = gensim.models.Doc2Vec(size=300, window=10, min_count=5, workers=11,alpha=0.025, min_alpha=0.025) # use fixed learning rate\n",
    "model.build_vocab(sentences)\n",
    "for epoch in range(10):\n",
    "    model.train(sentences, total_examples=model.corpus_count, epochs=model.iter)\n",
    "    model.alpha -= 0.002 # decrease the learning rate\n",
    "    model.min_alpha = model.alpha # fix the learning rate, no deca\n",
    "    model.train(it, total_examples=model.corpus_count, epochs=model.iter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'american': <gensim.models.keyedvectors.Vocab at 0x2610b63e438>,\n",
       " 'icon': <gensim.models.keyedvectors.Vocab at 0x2617bc18390>,\n",
       " 'section': <gensim.models.keyedvectors.Vocab at 0x2610b492668>,\n",
       " 'at': <gensim.models.keyedvectors.Vocab at 0x2610b503390>,\n",
       " 'the': <gensim.models.keyedvectors.Vocab at 0x2610bd24940>,\n",
       " 'museum': <gensim.models.keyedvectors.Vocab at 0x2610bd24b38>,\n",
       " 'centenary': <gensim.models.keyedvectors.Vocab at 0x2610bd24c50>,\n",
       " 'of': <gensim.models.keyedvectors.Vocab at 0x2610bd24a20>,\n",
       " 'her': <gensim.models.keyedvectors.Vocab at 0x2610bd24cc0>,\n",
       " 'birth': <gensim.models.keyedvectors.Vocab at 0x2610bd24cf8>,\n",
       " 'margaret': <gensim.models.keyedvectors.Vocab at 0x2610bd24d30>,\n",
       " 'mead': <gensim.models.keyedvectors.Vocab at 0x2610bd24da0>,\n",
       " 'is': <gensim.models.keyedvectors.Vocab at 0x2610bd24dd8>,\n",
       " 'remembered': <gensim.models.keyedvectors.Vocab at 0x2610bd24e10>,\n",
       " 'as': <gensim.models.keyedvectors.Vocab at 0x2610bd24198>,\n",
       " 'world': <gensim.models.keyedvectors.Vocab at 0x2610bd24048>,\n",
       " 'most': <gensim.models.keyedvectors.Vocab at 0x2610bd24080>,\n",
       " 'influential': <gensim.models.keyedvectors.Vocab at 0x2610bd240b8>,\n",
       " 'anthropologist': <gensim.models.keyedvectors.Vocab at 0x2610bd240f0>,\n",
       " 'arrived': <gensim.models.keyedvectors.Vocab at 0x2610bd24630>,\n",
       " 'natural': <gensim.models.keyedvectors.Vocab at 0x2610bd24240>,\n",
       " 'history': <gensim.models.keyedvectors.Vocab at 0x2610bd242b0>,\n",
       " 'in': <gensim.models.keyedvectors.Vocab at 0x2610bd243c8>,\n",
       " 'having': <gensim.models.keyedvectors.Vocab at 0x2610bd24438>,\n",
       " 'just': <gensim.models.keyedvectors.Vocab at 0x2610bd245f8>,\n",
       " 'completed': <gensim.models.keyedvectors.Vocab at 0x2610bd24710>,\n",
       " 'first': <gensim.models.keyedvectors.Vocab at 0x2610bd24780>,\n",
       " 'significant': <gensim.models.keyedvectors.Vocab at 0x2610bd247f0>,\n",
       " 'ethnographic': <gensim.models.keyedvectors.Vocab at 0x2610bd24978>,\n",
       " 'research': <gensim.models.keyedvectors.Vocab at 0x2610bd249b0>,\n",
       " 'samoa': <gensim.models.keyedvectors.Vocab at 0x2610b4f0b00>,\n",
       " 'she': <gensim.models.keyedvectors.Vocab at 0x2610b602c18>,\n",
       " 'was': <gensim.models.keyedvectors.Vocab at 0x2610b6502b0>,\n",
       " 'appointed': <gensim.models.keyedvectors.Vocab at 0x2610b650dd8>,\n",
       " 'assistant': <gensim.models.keyedvectors.Vocab at 0x2610b650860>,\n",
       " 'curator': <gensim.models.keyedvectors.Vocab at 0x2610b6503c8>,\n",
       " 'department': <gensim.models.keyedvectors.Vocab at 0x2610b7e05c0>,\n",
       " 'anthropology': <gensim.models.keyedvectors.Vocab at 0x2610b7e06a0>,\n",
       " 'only': <gensim.models.keyedvectors.Vocab at 0x2610b7e0710>,\n",
       " 'twenty': <gensim.models.keyedvectors.Vocab at 0x2610b7e0ba8>,\n",
       " 'five': <gensim.models.keyedvectors.Vocab at 0x2610b7e0b70>,\n",
       " 'years': <gensim.models.keyedvectors.Vocab at 0x2610b7e02b0>,\n",
       " 'old': <gensim.models.keyedvectors.Vocab at 0x2610b7e0198>,\n",
       " 'given': <gensim.models.keyedvectors.Vocab at 0x2610b7e0e10>,\n",
       " 'temporary': <gensim.models.keyedvectors.Vocab at 0x2610b7e0978>,\n",
       " 'office': <gensim.models.keyedvectors.Vocab at 0x2610b7e0a20>,\n",
       " 'an': <gensim.models.keyedvectors.Vocab at 0x2610b7e0e48>,\n",
       " 'attic': <gensim.models.keyedvectors.Vocab at 0x2610b7e0f28>,\n",
       " 'room': <gensim.models.keyedvectors.Vocab at 0x2610b7e0438>,\n",
       " 'west': <gensim.models.keyedvectors.Vocab at 0x2610b7e0f60>,\n",
       " 'tower': <gensim.models.keyedvectors.Vocab at 0x2610b7e00b8>,\n",
       " 'so': <gensim.models.keyedvectors.Vocab at 0x2610b7e0b00>,\n",
       " 'well': <gensim.models.keyedvectors.Vocab at 0x2610b7e0080>,\n",
       " 'did': <gensim.models.keyedvectors.Vocab at 0x2610b7e0128>,\n",
       " 'like': <gensim.models.keyedvectors.Vocab at 0x2610b7e0160>,\n",
       " 'this': <gensim.models.keyedvectors.Vocab at 0x2610b7e0240>,\n",
       " 'out': <gensim.models.keyedvectors.Vocab at 0x2610b7e0c88>,\n",
       " 'way': <gensim.models.keyedvectors.Vocab at 0x2610b7e0390>,\n",
       " 'with': <gensim.models.keyedvectors.Vocab at 0x2610b7e03c8>,\n",
       " 'its': <gensim.models.keyedvectors.Vocab at 0x2610b66a390>,\n",
       " 'views': <gensim.models.keyedvectors.Vocab at 0x2610b66a860>,\n",
       " 'rooftops': <gensim.models.keyedvectors.Vocab at 0x2610b66a0b8>,\n",
       " 'new': <gensim.models.keyedvectors.Vocab at 0x2610b66a358>,\n",
       " 'york': <gensim.models.keyedvectors.Vocab at 0x2610b66a320>,\n",
       " 'city': <gensim.models.keyedvectors.Vocab at 0x2610b66acc0>,\n",
       " 'and': <gensim.models.keyedvectors.Vocab at 0x2610b66a518>,\n",
       " 'two': <gensim.models.keyedvectors.Vocab at 0x2610b805f28>,\n",
       " 'stairways': <gensim.models.keyedvectors.Vocab at 0x2610b805748>,\n",
       " 'ideal': <gensim.models.keyedvectors.Vocab at 0x2610b8056a0>,\n",
       " 'said': <gensim.models.keyedvectors.Vocab at 0x2610b805550>,\n",
       " 'for': <gensim.models.keyedvectors.Vocab at 0x2610b805ac8>,\n",
       " 'creeping': <gensim.models.keyedvectors.Vocab at 0x2610b805b00>,\n",
       " 'down': <gensim.models.keyedvectors.Vocab at 0x2610b805c50>,\n",
       " 'one': <gensim.models.keyedvectors.Vocab at 0x2610b805c88>,\n",
       " 'stairway': <gensim.models.keyedvectors.Vocab at 0x2610b805d30>,\n",
       " 'while': <gensim.models.keyedvectors.Vocab at 0x2610b805d68>,\n",
       " 'someone': <gensim.models.keyedvectors.Vocab at 0x2610b805e80>,\n",
       " 'to': <gensim.models.keyedvectors.Vocab at 0x2610b805f60>,\n",
       " 'be': <gensim.models.keyedvectors.Vocab at 0x2610b805fd0>,\n",
       " 'avoided': <gensim.models.keyedvectors.Vocab at 0x2610b8051d0>,\n",
       " 'such': <gensim.models.keyedvectors.Vocab at 0x2610b805208>,\n",
       " 'too': <gensim.models.keyedvectors.Vocab at 0x2610b8050b8>,\n",
       " 'solicitous': <gensim.models.keyedvectors.Vocab at 0x2610b805400>,\n",
       " 'elderly': <gensim.models.keyedvectors.Vocab at 0x2610b805710>,\n",
       " 'laboring': <gensim.models.keyedvectors.Vocab at 0x2610b805358>,\n",
       " 'up': <gensim.models.keyedvectors.Vocab at 0x2610b8052b0>,\n",
       " 'other': <gensim.models.keyedvectors.Vocab at 0x2610b805320>,\n",
       " 'that': <gensim.models.keyedvectors.Vocab at 0x2610b805390>,\n",
       " 'stayed': <gensim.models.keyedvectors.Vocab at 0x2610b805438>,\n",
       " 'there': <gensim.models.keyedvectors.Vocab at 0x2610b8054e0>,\n",
       " 'rest': <gensim.models.keyedvectors.Vocab at 0x2610b805048>,\n",
       " 'life': <gensim.models.keyedvectors.Vocab at 0x2610b8056d8>,\n",
       " 'arriving': <gensim.models.keyedvectors.Vocab at 0x2610b498ef0>,\n",
       " 'same': <gensim.models.keyedvectors.Vocab at 0x2610b89bb00>,\n",
       " 'interviewed': <gensim.models.keyedvectors.Vocab at 0x2610b89b940>,\n",
       " 'by': <gensim.models.keyedvectors.Vocab at 0x2610b89b978>,\n",
       " 'position': <gensim.models.keyedvectors.Vocab at 0x2610b89bb70>,\n",
       " 'undergraduate': <gensim.models.keyedvectors.Vocab at 0x2610b89bba8>,\n",
       " 'major': <gensim.models.keyedvectors.Vocab at 0x2610b89bc18>,\n",
       " 'barnard': <gensim.models.keyedvectors.Vocab at 0x2610b89bc88>,\n",
       " 'college': <gensim.models.keyedvectors.Vocab at 0x2610b89bcf8>,\n",
       " 'felt': <gensim.models.keyedvectors.Vocab at 0x2610b89bd68>,\n",
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       " 'posthumously': <gensim.models.keyedvectors.Vocab at 0x2610b8862b0>,\n",
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       " 'jimmy': <gensim.models.keyedvectors.Vocab at 0x2610b886978>,\n",
       " 'carter': <gensim.models.keyedvectors.Vocab at 0x2610b8869e8>,\n",
       " 'signature': <gensim.models.keyedvectors.Vocab at 0x2610b886a20>,\n",
       " 'red': <gensim.models.keyedvectors.Vocab at 0x2610b886a58>,\n",
       " 'cape': <gensim.models.keyedvectors.Vocab at 0x2610b886ac8>,\n",
       " 'stick': <gensim.models.keyedvectors.Vocab at 0x2610b886b00>,\n",
       " 'label': <gensim.models.keyedvectors.Vocab at 0x2610b886ba8>,\n",
       " 'reads': <gensim.models.keyedvectors.Vocab at 0x2610b886be0>,\n",
       " 'later': <gensim.models.keyedvectors.Vocab at 0x2610b886c18>,\n",
       " 'regularly': <gensim.models.keyedvectors.Vocab at 0x2610b886c88>,\n",
       " 'wore': <gensim.models.keyedvectors.Vocab at 0x2610b8866d8>,\n",
       " 'used': <gensim.models.keyedvectors.Vocab at 0x2610b886780>,\n",
       " 'style': <gensim.models.keyedvectors.Vocab at 0x2610b8867f0>,\n",
       " 'walking': <gensim.models.keyedvectors.Vocab at 0x2610b886860>,\n",
       " 'often': <gensim.models.keyedvectors.Vocab at 0x2610b886898>,\n",
       " 'called': <gensim.models.keyedvectors.Vocab at 0x2610b5df4e0>,\n",
       " 'thumbstick': <gensim.models.keyedvectors.Vocab at 0x2610b5df940>,\n",
       " 'those': <gensim.models.keyedvectors.Vocab at 0x2610b5df748>,\n",
       " 'items': <gensim.models.keyedvectors.Vocab at 0x2610b5dfe10>,\n",
       " 'familiar': <gensim.models.keyedvectors.Vocab at 0x2610b5dfdd8>,\n",
       " 'sight': <gensim.models.keyedvectors.Vocab at 0x2610b5df198>,\n",
       " 'dr': <gensim.models.keyedvectors.Vocab at 0x2610b5df160>,\n",
       " 'traveled': <gensim.models.keyedvectors.Vocab at 0x2610b6a7908>,\n",
       " 'numerous': <gensim.models.keyedvectors.Vocab at 0x2610b510550>,\n",
       " 'joke': <gensim.models.keyedvectors.Vocab at 0x2610b8f6080>,\n",
       " 'want': <gensim.models.keyedvectors.Vocab at 0x2610b8f60b8>,\n",
       " 'recognized': <gensim.models.keyedvectors.Vocab at 0x2610b8f60f0>,\n",
       " 'all': <gensim.models.keyedvectors.Vocab at 0x2610b8f6128>,\n",
       " 'do': <gensim.models.keyedvectors.Vocab at 0x2610b8f6198>,\n",
       " 'leave': <gensim.models.keyedvectors.Vocab at 0x2610b8f61d0>,\n",
       " 'home': <gensim.models.keyedvectors.Vocab at 0x2610b8f6208>,\n",
       " 'how': <gensim.models.keyedvectors.Vocab at 0x2610b8f6278>,\n",
       " 'fitting': <gensim.models.keyedvectors.Vocab at 0x2610b8f62e8>,\n",
       " 'has': <gensim.models.keyedvectors.Vocab at 0x2610b8f6ba8>,\n",
       " 'become': <gensim.models.keyedvectors.Vocab at 0x2610b8f63c8>,\n",
       " 'artifact': <gensim.models.keyedvectors.Vocab at 0x2610b8f6400>,\n",
       " 'december': <gensim.models.keyedvectors.Vocab at 0x2610b8f6438>,\n",
       " 'lecture': <gensim.models.keyedvectors.Vocab at 0x2610b8f6390>,\n",
       " 'our': <gensim.models.keyedvectors.Vocab at 0x2610b8f64e0>,\n",
       " 'cosmic': <gensim.models.keyedvectors.Vocab at 0x2610b8f6358>,\n",
       " 'habitat': <gensim.models.keyedvectors.Vocab at 0x2610b8f6470>,\n",
       " 'distinguished': <gensim.models.keyedvectors.Vocab at 0x2610b8f6518>,\n",
       " 'authors': <gensim.models.keyedvectors.Vocab at 0x2610b8f6550>,\n",
       " 'astronomy': <gensim.models.keyedvectors.Vocab at 0x2610b8f65c0>,\n",
       " 'astronomer': <gensim.models.keyedvectors.Vocab at 0x2610b8f65f8>,\n",
       " 'royal': <gensim.models.keyedvectors.Vocab at 0x2610b8f6668>,\n",
       " 'martin': <gensim.models.keyedvectors.Vocab at 0x2610b8f66a0>,\n",
       " 'rees': <gensim.models.keyedvectors.Vocab at 0x2610b8f66d8>,\n",
       " 'professor': <gensim.models.keyedvectors.Vocab at 0x2610b8f6710>,\n",
       " 'cambridge': <gensim.models.keyedvectors.Vocab at 0x2610b8f67b8>,\n",
       " 'space': <gensim.models.keyedvectors.Vocab at 0x2610b8f67f0>,\n",
       " 'theater': <gensim.models.keyedvectors.Vocab at 0x2610b8f6860>,\n",
       " 'hayden': <gensim.models.keyedvectors.Vocab at 0x2610b8f6898>,\n",
       " 'planetarium': <gensim.models.keyedvectors.Vocab at 0x2610b8f6908>,\n",
       " 'panel': <gensim.models.keyedvectors.Vocab at 0x2610b8f6978>,\n",
       " 'discussion': <gensim.models.keyedvectors.Vocab at 0x2610b8f69b0>,\n",
       " 'using': <gensim.models.keyedvectors.Vocab at 0x2610b8f69e8>,\n",
       " 'genome': <gensim.models.keyedvectors.Vocab at 0x2610b8f6a20>,\n",
       " 'understand': <gensim.models.keyedvectors.Vocab at 0x2610b8f6a58>,\n",
       " 'ancestry': <gensim.models.keyedvectors.Vocab at 0x2610b8f6a90>,\n",
       " 'moderated': <gensim.models.keyedvectors.Vocab at 0x2610b8f6ac8>,\n",
       " 'nicholas': <gensim.models.keyedvectors.Vocab at 0x2610b8f6b00>,\n",
       " 'wade': <gensim.models.keyedvectors.Vocab at 0x2610b8f6b38>,\n",
       " 'times': <gensim.models.keyedvectors.Vocab at 0x2610b8f6b70>,\n",
       " 'biologist': <gensim.models.keyedvectors.Vocab at 0x2610b8f6be0>,\n",
       " 'andrew': <gensim.models.keyedvectors.Vocab at 0x2610b8f6c18>,\n",
       " 'clark': <gensim.models.keyedvectors.Vocab at 0x2610b8f6c50>,\n",
       " 'pennsylvania': <gensim.models.keyedvectors.Vocab at 0x2610b8f6c88>,\n",
       " 'state': <gensim.models.keyedvectors.Vocab at 0x2610b8f6cf8>,\n",
       " 'douglas': <gensim.models.keyedvectors.Vocab at 0x2610b8f6d68>,\n",
       " 'wallace': <gensim.models.keyedvectors.Vocab at 0x2610b8f6da0>,\n",
       " 'center': <gensim.models.keyedvectors.Vocab at 0x2610b8f6dd8>,\n",
       " 'molecular': <gensim.models.keyedvectors.Vocab at 0x2610b8f6e10>,\n",
       " 'medicine': <gensim.models.keyedvectors.Vocab at 0x2610b8f6e48>,\n",
       " 'emory': <gensim.models.keyedvectors.Vocab at 0x2610b8f6e80>,\n",
       " 'evolutionary': <gensim.models.keyedvectors.Vocab at 0x2610b8f6eb8>,\n",
       " 'joseph': <gensim.models.keyedvectors.Vocab at 0x2610b8f6ef0>,\n",
       " 'graves': <gensim.models.keyedvectors.Vocab at 0x2610b8f6f28>,\n",
       " 'jr': <gensim.models.keyedvectors.Vocab at 0x2610b8f6f60>,\n",
       " 'arizona': <gensim.models.keyedvectors.Vocab at 0x2610b8f6f98>,\n",
       " 'kaufmann': <gensim.models.keyedvectors.Vocab at 0x2610b8f6fd0>,\n",
       " 'conjunction': <gensim.models.keyedvectors.Vocab at 0x2610b8f6320>,\n",
       " 'genomic': <gensim.models.keyedvectors.Vocab at 0x2610b8f6048>,\n",
       " 'revolution': <gensim.models.keyedvectors.Vocab at 0x2610b7c0390>,\n",
       " 'january': <gensim.models.keyedvectors.Vocab at 0x2610b7c0c88>,\n",
       " 'gallery': <gensim.models.keyedvectors.Vocab at 0x2610b7c0b70>,\n",
       " 'workshops': <gensim.models.keyedvectors.Vocab at 0x2610b7c0470>,\n",
       " 'genetics': <gensim.models.keyedvectors.Vocab at 0x2610b7c0710>,\n",
       " 'follow': <gensim.models.keyedvectors.Vocab at 0x2610b7c0780>,\n",
       " 'explanation': <gensim.models.keyedvectors.Vocab at 0x2610b7c06d8>,\n",
       " 'session': <gensim.models.keyedvectors.Vocab at 0x2610b7c0f28>,\n",
       " 'roundtable': <gensim.models.keyedvectors.Vocab at 0x2610b850828>,\n",
       " 'presentation': <gensim.models.keyedvectors.Vocab at 0x2610b850eb8>,\n",
       " 'border': <gensim.models.keyedvectors.Vocab at 0x2610b8509b0>,\n",
       " 'crossing': <gensim.models.keyedvectors.Vocab at 0x2610b850b38>,\n",
       " 'cultural': <gensim.models.keyedvectors.Vocab at 0x2610b850c18>,\n",
       " 'appropriation': <gensim.models.keyedvectors.Vocab at 0x2610b850c88>,\n",
       " 'artistic': <gensim.models.keyedvectors.Vocab at 0x2610b850da0>,\n",
       " 'arts': <gensim.models.keyedvectors.Vocab at 0x2610b850cc0>,\n",
       " 'literature': <gensim.models.keyedvectors.Vocab at 0x2610b850dd8>,\n",
       " 'indigenous': <gensim.models.keyedvectors.Vocab at 0x2610b850320>,\n",
       " 'latina': <gensim.models.keyedvectors.Vocab at 0x2610b850198>,\n",
       " 'women': <gensim.models.keyedvectors.Vocab at 0x2610b850ba8>,\n",
       " 'identities': <gensim.models.keyedvectors.Vocab at 0x2610b850e48>,\n",
       " 'beyond': <gensim.models.keyedvectors.Vocab at 0x2610b850208>,\n",
       " 'borders': <gensim.models.keyedvectors.Vocab at 0x2610b850470>,\n",
       " 'writer': <gensim.models.keyedvectors.Vocab at 0x2610b8504a8>,\n",
       " 'helena': <gensim.models.keyedvectors.Vocab at 0x2610b850518>,\n",
       " 'maria': <gensim.models.keyedvectors.Vocab at 0x2610b850550>,\n",
       " 'cornell': <gensim.models.keyedvectors.Vocab at 0x2610b8505c0>,\n",
       " 'artist': <gensim.models.keyedvectors.Vocab at 0x2610b850630>,\n",
       " 'participants': <gensim.models.keyedvectors.Vocab at 0x2610b8506a0>,\n",
       " 'dominguez': <gensim.models.keyedvectors.Vocab at 0x2610b850748>,\n",
       " 'carol': <gensim.models.keyedvectors.Vocab at 0x2610b7da978>,\n",
       " 'kinder': <gensim.models.keyedvectors.Vocab at 0x2610b7da6a0>,\n",
       " 'visit': <gensim.models.keyedvectors.Vocab at 0x2610b7da2b0>,\n",
       " 'who': <gensim.models.keyedvectors.Vocab at 0x2610b7da9b0>,\n",
       " 'virgin': <gensim.models.keyedvectors.Vocab at 0x2610b7daa90>,\n",
       " 'guadalupe': <gensim.models.keyedvectors.Vocab at 0x2610b7daac8>,\n",
       " 'artists': <gensim.models.keyedvectors.Vocab at 0x2610b7dabe0>,\n",
       " 'henry': <gensim.models.keyedvectors.Vocab at 0x2610b7dac18>,\n",
       " 'street': <gensim.models.keyedvectors.Vocab at 0x2610b7dac50>,\n",
       " 'settlement': <gensim.models.keyedvectors.Vocab at 0x2610b7dac88>,\n",
       " 'art': <gensim.models.keyedvectors.Vocab at 0x2610b7dacc0>,\n",
       " 'shuttle': <gensim.models.keyedvectors.Vocab at 0x2610b7dada0>,\n",
       " 'amnh': <gensim.models.keyedvectors.Vocab at 0x2610b7dadd8>,\n",
       " 'or': <gensim.models.keyedvectors.Vocab at 0x2610b7dae10>,\n",
       " 'www': <gensim.models.keyedvectors.Vocab at 0x2610b7daeb8>,\n",
       " 'org': <gensim.models.keyedvectors.Vocab at 0x2610b7da438>,\n",
       " 'programs': <gensim.models.keyedvectors.Vocab at 0x2610b7da2e8>,\n",
       " 'hubble': <gensim.models.keyedvectors.Vocab at 0x2610b7da358>,\n",
       " 'machine': <gensim.models.keyedvectors.Vocab at 0x2610b7da390>,\n",
       " 'frontiers': <gensim.models.keyedvectors.Vocab at 0x2610b7da320>,\n",
       " 'astrophysics': <gensim.models.keyedvectors.Vocab at 0x2610b7da4e0>,\n",
       " 'james': <gensim.models.keyedvectors.Vocab at 0x2610b7da550>,\n",
       " 'massachusetts': <gensim.models.keyedvectors.Vocab at 0x2610b7da9e8>,\n",
       " 'amherst': <gensim.models.keyedvectors.Vocab at 0x2610b7da898>,\n",
       " 'upcoming': <gensim.models.keyedvectors.Vocab at 0x2610b7dae80>,\n",
       " 'sky': <gensim.models.keyedvectors.Vocab at 0x2610b7da780>,\n",
       " 'events': <gensim.models.keyedvectors.Vocab at 0x2610b7da860>,\n",
       " 'celestial': <gensim.models.keyedvectors.Vocab at 0x2610b444198>,\n",
       " 'highlights': <gensim.models.keyedvectors.Vocab at 0x2610b5cd400>,\n",
       " 'lecturer': <gensim.models.keyedvectors.Vocab at 0x2610b5d9ba8>,\n",
       " 'amateur': <gensim.models.keyedvectors.Vocab at 0x2617518cf60>,\n",
       " 'hank': <gensim.models.keyedvectors.Vocab at 0x2610b8e5080>,\n",
       " 'bartol': <gensim.models.keyedvectors.Vocab at 0x2610b8e50b8>,\n",
       " 'kwanzaa': <gensim.models.keyedvectors.Vocab at 0x2610b8e5208>,\n",
       " 'celebration': <gensim.models.keyedvectors.Vocab at 0x2610b8e5240>,\n",
       " 'performances': <gensim.models.keyedvectors.Vocab at 0x2610b8e5278>,\n",
       " 'marketplace': <gensim.models.keyedvectors.Vocab at 0x2610b8e52b0>,\n",
       " 'african': <gensim.models.keyedvectors.Vocab at 0x2610b8e52e8>,\n",
       " 'holiday': <gensim.models.keyedvectors.Vocab at 0x2610b8e5358>,\n",
       " 'ocean': <gensim.models.keyedvectors.Vocab at 0x2610b8e53c8>,\n",
       " 'schedule': <gensim.models.keyedvectors.Vocab at 0x2610b8e5400>,\n",
       " 'call': <gensim.models.keyedvectors.Vocab at 0x2610b8e5438>,\n",
       " 'riddle': <gensim.models.keyedvectors.Vocab at 0x2610b8e5470>,\n",
       " 'compass': <gensim.models.keyedvectors.Vocab at 0x2610b8e54a8>,\n",
       " 'mathematician': <gensim.models.keyedvectors.Vocab at 0x2610b8e54e0>,\n",
       " 'amir': <gensim.models.keyedvectors.Vocab at 0x2610b8e5550>,\n",
       " 'weekend': <gensim.models.keyedvectors.Vocab at 0x2610b8e5588>,\n",
       " 'america': <gensim.models.keyedvectors.Vocab at 0x2610b8e55c0>,\n",
       " 'arab': <gensim.models.keyedvectors.Vocab at 0x2610b8e55f8>,\n",
       " 'asian': <gensim.models.keyedvectors.Vocab at 0x2610b8e5630>,\n",
       " 'community': <gensim.models.keyedvectors.Vocab at 0x2610b8e5668>,\n",
       " 'based': <gensim.models.keyedvectors.Vocab at 0x2610b8e56a0>,\n",
       " 'present': <gensim.models.keyedvectors.Vocab at 0x2610b8e56d8>,\n",
       " 'films': <gensim.models.keyedvectors.Vocab at 0x2610b8e5710>,\n",
       " 'talks': <gensim.models.keyedvectors.Vocab at 0x2610b8e5748>,\n",
       " 'discussions': <gensim.models.keyedvectors.Vocab at 0x2610b8e5780>,\n",
       " 'readings': <gensim.models.keyedvectors.Vocab at 0x2610b8e5e80>,\n",
       " 'leonhardt': <gensim.models.keyedvectors.Vocab at 0x2610b8e57b8>,\n",
       " 'frederick': <gensim.models.keyedvectors.Vocab at 0x2610b8e57f0>,\n",
       " 'rose': <gensim.models.keyedvectors.Vocab at 0x2610b8e5ef0>,\n",
       " 'director': <gensim.models.keyedvectors.Vocab at 0x2610b8e5f28>,\n",
       " 'charles': <gensim.models.keyedvectors.Vocab at 0x2610b8e5e10>,\n",
       " 'liu': <gensim.models.keyedvectors.Vocab at 0x2610b8e5f98>,\n",
       " 'demonstration': <gensim.models.keyedvectors.Vocab at 0x2610b8e5f60>,\n",
       " 'following': <gensim.models.keyedvectors.Vocab at 0x2610b8e5dd8>,\n",
       " 'beat': <gensim.models.keyedvectors.Vocab at 0x2610b8e5eb8>,\n",
       " 'instruments': <gensim.models.keyedvectors.Vocab at 0x2610b8e5fd0>,\n",
       " 'neil': <gensim.models.keyedvectors.Vocab at 0x2610b8e5048>,\n",
       " 'clarke': <gensim.models.keyedvectors.Vocab at 0x2610b8e5128>,\n",
       " 'origin': <gensim.models.keyedvectors.Vocab at 0x2610b8e5b38>,\n",
       " 'evolution': <gensim.models.keyedvectors.Vocab at 0x2610b8e51d0>,\n",
       " 'sea': <gensim.models.keyedvectors.Vocab at 0x2610b8e5860>,\n",
       " 'marine': <gensim.models.keyedvectors.Vocab at 0x2610b8e5898>,\n",
       " 'richard': <gensim.models.keyedvectors.Vocab at 0x2610b8e5908>,\n",
       " 'ellis': <gensim.models.keyedvectors.Vocab at 0x2610b8e5940>,\n",
       " 'division': <gensim.models.keyedvectors.Vocab at 0x2610b8e5978>,\n",
       " 'paleontology': <gensim.models.keyedvectors.Vocab at 0x2610b8e59b0>,\n",
       " 'during': <gensim.models.keyedvectors.Vocab at 0x2610b8e59e8>,\n",
       " 'origami': <gensim.models.keyedvectors.Vocab at 0x2610b8e5a58>,\n",
       " 'tree': <gensim.models.keyedvectors.Vocab at 0x2610b8e5a90>,\n",
       " 'decorated': <gensim.models.keyedvectors.Vocab at 0x2610b8e5ac8>,\n",
       " 'folded': <gensim.models.keyedvectors.Vocab at 0x2610b8e5b00>,\n",
       " 'paper': <gensim.models.keyedvectors.Vocab at 0x2610b8e5b70>,\n",
       " 'animals': <gensim.models.keyedvectors.Vocab at 0x2610b8e5ba8>,\n",
       " 'display': <gensim.models.keyedvectors.Vocab at 0x2610b8e5be0>,\n",
       " 'theodore': <gensim.models.keyedvectors.Vocab at 0x2610b8e5c18>,\n",
       " 'roosevelt': <gensim.models.keyedvectors.Vocab at 0x2610b8e5c50>,\n",
       " 'memorial': <gensim.models.keyedvectors.Vocab at 0x2610b8e5cc0>,\n",
       " 'teachers': <gensim.models.keyedvectors.Vocab at 0x2610b8e5cf8>,\n",
       " 'resource': <gensim.models.keyedvectors.Vocab at 0x2610b8e5d68>,\n",
       " 'musings': <gensim.models.keyedvectors.Vocab at 0x2610b8e5da0>,\n",
       " 'web': <gensim.models.keyedvectors.Vocab at 0x2610b79dda0>,\n",
       " 'newsletter': <gensim.models.keyedvectors.Vocab at 0x2610b79def0>,\n",
       " 'science': <gensim.models.keyedvectors.Vocab at 0x2610b79d1d0>,\n",
       " 'educators': <gensim.models.keyedvectors.Vocab at 0x2610b79d630>,\n",
       " 'toolong': <gensim.models.keyedvectors.Vocab at 0x2610b79d940>,\n",
       " 'without': <gensim.models.keyedvectors.Vocab at 0x2610b79d518>,\n",
       " 'walls': <gensim.models.keyedvectors.Vocab at 0x2610b79dac8>,\n",
       " 'telephone': <gensim.models.keyedvectors.Vocab at 0x2610b79db70>,\n",
       " 'sciences': <gensim.models.keyedvectors.Vocab at 0x2610b79d198>,\n",
       " 'education': <gensim.models.keyedvectors.Vocab at 0x2610b79d5f8>,\n",
       " 'nonprofit': <gensim.models.keyedvectors.Vocab at 0x2610b79d320>,\n",
       " 'serving': <gensim.models.keyedvectors.Vocab at 0x2610b79dc50>,\n",
       " 'information': <gensim.models.keyedvectors.Vocab at 0x2610b79d7b8>,\n",
       " 'toll': <gensim.models.keyedvectors.Vocab at 0x2617b93e6d8>,\n",
       " 'free': <gensim.models.keyedvectors.Vocab at 0x2610b627080>,\n",
       " 'adults': <gensim.models.keyedvectors.Vocab at 0x2610b627cc0>,\n",
       " 'inside': <gensim.models.keyedvectors.Vocab at 0x2610b441a90>,\n",
       " 'outside': <gensim.models.keyedvectors.Vocab at 0x2610b8ee2b0>,\n",
       " 'beginning': <gensim.models.keyedvectors.Vocab at 0x2610b8ee198>,\n",
       " 'choose': <gensim.models.keyedvectors.Vocab at 0x2610b8eef98>,\n",
       " 'telescope': <gensim.models.keyedvectors.Vocab at 0x2610b8ee048>,\n",
       " 'stars': <gensim.models.keyedvectors.Vocab at 0x2610b8ee0f0>,\n",
       " 'constellations': <gensim.models.keyedvectors.Vocab at 0x2610b8ee6d8>,\n",
       " 'legends': <gensim.models.keyedvectors.Vocab at 0x2610b8eed30>,\n",
       " 'many': <gensim.models.keyedvectors.Vocab at 0x2610b8ee080>,\n",
       " 'others': <gensim.models.keyedvectors.Vocab at 0x2610b8ee0b8>,\n",
       " 'complete': <gensim.models.keyedvectors.Vocab at 0x2610b8ee128>,\n",
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       " 'terms': <gensim.models.keyedvectors.Vocab at 0x2610b9cfe80>,\n",
       " 'survival': <gensim.models.keyedvectors.Vocab at 0x2610b9cfeb8>,\n",
       " 'darwinian': <gensim.models.keyedvectors.Vocab at 0x2610b9cfef0>,\n",
       " 'fitness': <gensim.models.keyedvectors.Vocab at 0x2610b9cff28>,\n",
       " 'according': <gensim.models.keyedvectors.Vocab at 0x2610b9cf160>,\n",
       " 'if': <gensim.models.keyedvectors.Vocab at 0x2610b9cff98>,\n",
       " 'spent': <gensim.models.keyedvectors.Vocab at 0x2610b9cf358>,\n",
       " 'lot': <gensim.models.keyedvectors.Vocab at 0x2610b9cf390>,\n",
       " 'calories': <gensim.models.keyedvectors.Vocab at 0x2610b9cfe48>,\n",
       " 'growing': <gensim.models.keyedvectors.Vocab at 0x2610b9cf4e0>,\n",
       " 'biggest': <gensim.models.keyedvectors.Vocab at 0x2610b9cf588>,\n",
       " 'whatever': <gensim.models.keyedvectors.Vocab at 0x2610b9cf710>,\n",
       " 'could': <gensim.models.keyedvectors.Vocab at 0x2610b9cf748>,\n",
       " 'possibly': <gensim.models.keyedvectors.Vocab at 0x2610b9cf780>,\n",
       " 'enough': <gensim.models.keyedvectors.Vocab at 0x2610b9cf860>,\n",
       " 'sensible': <gensim.models.keyedvectors.Vocab at 0x2610b9cfa58>,\n",
       " 'keeping': <gensim.models.keyedvectors.Vocab at 0x2610b9cfa90>,\n",
       " 'immune': <gensim.models.keyedvectors.Vocab at 0x2610b9cfb70>,\n",
       " 'system': <gensim.models.keyedvectors.Vocab at 0x2610b9cfe10>,\n",
       " 'tuned': <gensim.models.keyedvectors.Vocab at 0x2610b6426a0>,\n",
       " 'generally': <gensim.models.keyedvectors.Vocab at 0x2610b57c6d8>,\n",
       " 'fallen': <gensim.models.keyedvectors.Vocab at 0x2610b57c4a8>,\n",
       " 'favor': <gensim.models.keyedvectors.Vocab at 0x2610b57c7f0>,\n",
       " 'striking': <gensim.models.keyedvectors.Vocab at 0x2610b7a95f8>,\n",
       " 'support': <gensim.models.keyedvectors.Vocab at 0x2610b7a9630>,\n",
       " 'emerged': <gensim.models.keyedvectors.Vocab at 0x2610b7a9390>,\n",
       " 'robert': <gensim.models.keyedvectors.Vocab at 0x2610b7a96d8>,\n",
       " 'brooks': <gensim.models.keyedvectors.Vocab at 0x2610b7a9f60>,\n",
       " 'john': <gensim.models.keyedvectors.Vocab at 0x2610b7a9c50>,\n",
       " 'endler': <gensim.models.keyedvectors.Vocab at 0x2610b7a9588>,\n",
       " 'cook': <gensim.models.keyedvectors.Vocab at 0x2610b5047f0>,\n",
       " 'australia': <gensim.models.keyedvectors.Vocab at 0x2610b7d0f60>,\n",
       " 'attractiveness': <gensim.models.keyedvectors.Vocab at 0x2610b7d0128>,\n",
       " 'guppies': <gensim.models.keyedvectors.Vocab at 0x2610b7d0c18>,\n",
       " 'they': <gensim.models.keyedvectors.Vocab at 0x2610b7d0e48>,\n",
       " 'showed': <gensim.models.keyedvectors.Vocab at 0x2610b7d0470>,\n",
       " 'color': <gensim.models.keyedvectors.Vocab at 0x2610b7d0d30>,\n",
       " 'patterns': <gensim.models.keyedvectors.Vocab at 0x2610b7d0eb8>,\n",
       " 'considered': <gensim.models.keyedvectors.Vocab at 0x2610b7d0208>,\n",
       " 'attractive': <gensim.models.keyedvectors.Vocab at 0x2610b7d02b0>,\n",
       " 'sired': <gensim.models.keyedvectors.Vocab at 0x2610b7d0320>,\n",
       " 'sons': <gensim.models.keyedvectors.Vocab at 0x2610b7d0390>,\n",
       " 'especially': <gensim.models.keyedvectors.Vocab at 0x2610b7d03c8>,\n",
       " 'patterning': <gensim.models.keyedvectors.Vocab at 0x2610b7d0400>,\n",
       " 'scientists': <gensim.models.keyedvectors.Vocab at 0x2610b7d0518>,\n",
       " 'determined': <gensim.models.keyedvectors.Vocab at 0x2610b7d0630>,\n",
       " 'heritable': <gensim.models.keyedvectors.Vocab at 0x2610b7d06d8>,\n",
       " 'may': <gensim.models.keyedvectors.Vocab at 0x2610b7d0be0>,\n",
       " 'coded': <gensim.models.keyedvectors.Vocab at 0x2610b7d0710>,\n",
       " 'cluster': <gensim.models.keyedvectors.Vocab at 0x2610b7d07b8>,\n",
       " 'chromosome': <gensim.models.keyedvectors.Vocab at 0x2610b7d0828>,\n",
       " 'then': <gensim.models.keyedvectors.Vocab at 0x2610b7d0668>,\n",
       " 'july': <gensim.models.keyedvectors.Vocab at 0x2610b7d0b70>,\n",
       " 'significantly': <gensim.models.keyedvectors.Vocab at 0x2610b7d0898>,\n",
       " 'less': <gensim.models.keyedvectors.Vocab at 0x2610b7d08d0>,\n",
       " 'survive': <gensim.models.keyedvectors.Vocab at 0x2610b7d0940>,\n",
       " 'average': <gensim.models.keyedvectors.Vocab at 0x2610b7d0978>,\n",
       " 'case': <gensim.models.keyedvectors.Vocab at 0x2610b7d0a90>,\n",
       " 'once': <gensim.models.keyedvectors.Vocab at 0x2610b7d0ac8>,\n",
       " 'get': <gensim.models.keyedvectors.Vocab at 0x2610b7d0c50>,\n",
       " 'killed': <gensim.models.keyedvectors.Vocab at 0x2610b570668>,\n",
       " 'predator': <gensim.models.keyedvectors.Vocab at 0x2610b5704e0>,\n",
       " 'cost': <gensim.models.keyedvectors.Vocab at 0x2610b570240>,\n",
       " 'being': <gensim.models.keyedvectors.Vocab at 0x2610b5706a0>,\n",
       " 'flashy': <gensim.models.keyedvectors.Vocab at 0x2610b570c50>,\n",
       " 'conspicuous': <gensim.models.keyedvectors.Vocab at 0x2610b570e48>,\n",
       " 'lower': <gensim.models.keyedvectors.Vocab at 0x2610b570dd8>,\n",
       " 'reach': <gensim.models.keyedvectors.Vocab at 0x2610b570550>,\n",
       " 'maturity': <gensim.models.keyedvectors.Vocab at 0x2610b5701d0>,\n",
       " 'develop': <gensim.models.keyedvectors.Vocab at 0x2610b570400>,\n",
       " 'coloration': <gensim.models.keyedvectors.Vocab at 0x2610b696f28>,\n",
       " 'experiment': <gensim.models.keyedvectors.Vocab at 0x2610b696ba8>,\n",
       " 'stands': <gensim.models.keyedvectors.Vocab at 0x2610b696780>,\n",
       " 'another': <gensim.models.keyedvectors.Vocab at 0x2610b696b70>,\n",
       " 'decorations': <gensim.models.keyedvectors.Vocab at 0x2610b696240>,\n",
       " 'bad': <gensim.models.keyedvectors.Vocab at 0x2610b8b4ac8>,\n",
       " 'say': <gensim.models.keyedvectors.Vocab at 0x2610b8b47f0>,\n",
       " 'fads': <gensim.models.keyedvectors.Vocab at 0x2610b8b4860>,\n",
       " 'jacob': <gensim.models.keyedvectors.Vocab at 0x2610b8b4898>,\n",
       " 'arne': <gensim.models.keyedvectors.Vocab at 0x2610b8b48d0>,\n",
       " 'lundberg': <gensim.models.keyedvectors.Vocab at 0x2610b8b4d68>,\n",
       " 'uppsala': <gensim.models.keyedvectors.Vocab at 0x2610b8b4828>,\n",
       " 'sweden': <gensim.models.keyedvectors.Vocab at 0x2610b8b4908>,\n",
       " 'thought': <gensim.models.keyedvectors.Vocab at 0x2610b8b49b0>,\n",
       " 'interesting': <gensim.models.keyedvectors.Vocab at 0x2610b8b49e8>,\n",
       " 'black': <gensim.models.keyedvectors.Vocab at 0x2610b8b4a20>,\n",
       " 'grouse': <gensim.models.keyedvectors.Vocab at 0x2610b8b4a58>,\n",
       " 'chemistry': <gensim.models.keyedvectors.Vocab at 0x2610b8b4160>,\n",
       " 'interested': <gensim.models.keyedvectors.Vocab at 0x2610b8b4198>,\n",
       " 'use': <gensim.models.keyedvectors.Vocab at 0x2610b8b41d0>,\n",
       " 'low': <gensim.models.keyedvectors.Vocab at 0x2610b8b4208>,\n",
       " 'tech': <gensim.models.keyedvectors.Vocab at 0x2610b8b4278>,\n",
       " 'scientific': <gensim.models.keyedvectors.Vocab at 0x2610b8b42b0>,\n",
       " 'artifice': <gensim.models.keyedvectors.Vocab at 0x2610b8b42e8>,\n",
       " 'turn': <gensim.models.keyedvectors.Vocab at 0x2610b8b4320>,\n",
       " 'appears': <gensim.models.keyedvectors.Vocab at 0x2610b8b4358>,\n",
       " 'highly': <gensim.models.keyedvectors.Vocab at 0x2610b8b4390>,\n",
       " 'surround': <gensim.models.keyedvectors.Vocab at 0x2610b8b4438>,\n",
       " 'indifferent': <gensim.models.keyedvectors.Vocab at 0x2610b8b44e0>,\n",
       " 'suddenly': <gensim.models.keyedvectors.Vocab at 0x2610b8b4550>,\n",
       " 'decides': <gensim.models.keyedvectors.Vocab at 0x2610b8b45c0>,\n",
       " 'kinda': <gensim.models.keyedvectors.Vocab at 0x2610b8b4630>,\n",
       " 'cute': <gensim.models.keyedvectors.Vocab at 0x2610b8b44a8>,\n",
       " 'bandwagon': <gensim.models.keyedvectors.Vocab at 0x2610b8b4400>,\n",
       " 'effect': <gensim.models.keyedvectors.Vocab at 0x2610b8b40b8>,\n",
       " 'seems': <gensim.models.keyedvectors.Vocab at 0x2610b8b4b00>,\n",
       " 'mean': <gensim.models.keyedvectors.Vocab at 0x2610b8b4c88>,\n",
       " 'your': <gensim.models.keyedvectors.Vocab at 0x2610b8b4710>,\n",
       " 'decide': <gensim.models.keyedvectors.Vocab at 0x2610b8b4da0>,\n",
       " 'neon': <gensim.models.keyedvectors.Vocab at 0x2610b8b4dd8>,\n",
       " 'hot': <gensim.models.keyedvectors.Vocab at 0x2610b8b4e10>,\n",
       " 'commodity': <gensim.models.keyedvectors.Vocab at 0x2610b8b4fd0>,\n",
       " 'think': <gensim.models.keyedvectors.Vocab at 0x2610b8b4e48>,\n",
       " 'look': <gensim.models.keyedvectors.Vocab at 0x2610b8b4e80>,\n",
       " 'ridiculous': <gensim.models.keyedvectors.Vocab at 0x2610b8b4eb8>,\n",
       " 'advantage': <gensim.models.keyedvectors.Vocab at 0x2610b8b4ef0>,\n",
       " 'rage': <gensim.models.keyedvectors.Vocab at 0x2610b8b4f28>,\n",
       " 'order': <gensim.models.keyedvectors.Vocab at 0x2610b8b4f60>,\n",
       " 'maximize': <gensim.models.keyedvectors.Vocab at 0x2610bd1f860>,\n",
       " 'passing': <gensim.models.keyedvectors.Vocab at 0x2610bd1f390>,\n",
       " 'copies': <gensim.models.keyedvectors.Vocab at 0x2610bd1f048>,\n",
       " 'circular': <gensim.models.keyedvectors.Vocab at 0x2610bd1fac8>,\n",
       " 'logic': <gensim.models.keyedvectors.Vocab at 0x2610bd1ff28>,\n",
       " 'trait': <gensim.models.keyedvectors.Vocab at 0x2610bd1fda0>,\n",
       " 'becomes': <gensim.models.keyedvectors.Vocab at 0x2610b55d3c8>,\n",
       " 'carries': <gensim.models.keyedvectors.Vocab at 0x2610b55d6d8>,\n",
       " 'carrier': <gensim.models.keyedvectors.Vocab at 0x2610b55d470>,\n",
       " 'still': <gensim.models.keyedvectors.Vocab at 0x2610b82ff28>,\n",
       " 'possibility': <gensim.models.keyedvectors.Vocab at 0x2610b82fd30>,\n",
       " 'really': <gensim.models.keyedvectors.Vocab at 0x2610b82fbe0>,\n",
       " 'translate': <gensim.models.keyedvectors.Vocab at 0x2610b82ff60>,\n",
       " 'meaningful': <gensim.models.keyedvectors.Vocab at 0x2610b82f9b0>,\n",
       " 'message': <gensim.models.keyedvectors.Vocab at 0x2610b82fc88>,\n",
       " 'can': <gensim.models.keyedvectors.Vocab at 0x2610b82f400>,\n",
       " 'bet': <gensim.models.keyedvectors.Vocab at 0x2610b82f198>,\n",
       " 'afford': <gensim.models.keyedvectors.Vocab at 0x2610b82f240>,\n",
       " 'waste': <gensim.models.keyedvectors.Vocab at 0x2610b82f278>,\n",
       " 'three': <gensim.models.keyedvectors.Vocab at 0x2610b82f2e8>,\n",
       " 'foot': <gensim.models.keyedvectors.Vocab at 0x2610b82f358>,\n",
       " 'feathers': <gensim.models.keyedvectors.Vocab at 0x2610b82f390>,\n",
       " 'marlene': <gensim.models.keyedvectors.Vocab at 0x2610b82f470>,\n",
       " 'biology': <gensim.models.keyedvectors.Vocab at 0x2610b82f4a8>,\n",
       " 'formalized': <gensim.models.keyedvectors.Vocab at 0x2610b82fa58>,\n",
       " 'notion': <gensim.models.keyedvectors.Vocab at 0x2610b82f3c8>,\n",
       " 'expensive': <gensim.models.keyedvectors.Vocab at 0x2610b82f710>,\n",
       " 'signaled': <gensim.models.keyedvectors.Vocab at 0x2610b82f748>,\n",
       " 'parasites': <gensim.models.keyedvectors.Vocab at 0x2610b82f7b8>,\n",
       " 'charming': <gensim.models.keyedvectors.Vocab at 0x2610b82f7f0>,\n",
       " 'response': <gensim.models.keyedvectors.Vocab at 0x2610b82f828>,\n",
       " 'april': <gensim.models.keyedvectors.Vocab at 0x2610b82f898>,\n",
       " 'why': <gensim.models.keyedvectors.Vocab at 0x2610b82fac8>,\n",
       " 'find': <gensim.models.keyedvectors.Vocab at 0x2610b82fb00>,\n",
       " 'appealing': <gensim.models.keyedvectors.Vocab at 0x2610b82fb38>,\n",
       " 'decreases': <gensim.models.keyedvectors.Vocab at 0x2610b82fb70>,\n",
       " 'likelihood': <gensim.models.keyedvectors.Vocab at 0x2610b82fba8>,\n",
       " 'll': <gensim.models.keyedvectors.Vocab at 0x2610b82f908>,\n",
       " 'wind': <gensim.models.keyedvectors.Vocab at 0x2610b82f940>,\n",
       " 'guy': <gensim.models.keyedvectors.Vocab at 0x2610b82f978>,\n",
       " 'sexually': <gensim.models.keyedvectors.Vocab at 0x2610b82f668>,\n",
       " 'reproducing': <gensim.models.keyedvectors.Vocab at 0x2610b82fcf8>,\n",
       " 'organisms': <gensim.models.keyedvectors.Vocab at 0x2610b82fd68>,\n",
       " 'need': <gensim.models.keyedvectors.Vocab at 0x2610b82fe10>,\n",
       " 'worry': <gensim.models.keyedvectors.Vocab at 0x2610b82fe80>,\n",
       " 'transmitted': <gensim.models.keyedvectors.Vocab at 0x2610b82feb8>,\n",
       " 'diseases': <gensim.models.keyedvectors.Vocab at 0x2610b8cdd30>,\n",
       " 'evolutionarily': <gensim.models.keyedvectors.Vocab at 0x2610b8cd0b8>,\n",
       " 'version': <gensim.models.keyedvectors.Vocab at 0x2610b8cd0f0>,\n",
       " 'basic': <gensim.models.keyedvectors.Vocab at 0x2610b8cd128>,\n",
       " 'theme': <gensim.models.keyedvectors.Vocab at 0x2610b8cd160>,\n",
       " 'signals': <gensim.models.keyedvectors.Vocab at 0x2610b8cd1d0>,\n",
       " 'passed': <gensim.models.keyedvectors.Vocab at 0x2610b8cd208>,\n",
       " 'next': <gensim.models.keyedvectors.Vocab at 0x2610b8cd240>,\n",
       " 'generation': <gensim.models.keyedvectors.Vocab at 0x2610b8cd278>,\n",
       " 'zahavi': <gensim.models.keyedvectors.Vocab at 0x2610b8cd2b0>,\n",
       " 'tel': <gensim.models.keyedvectors.Vocab at 0x2610b8cd2e8>,\n",
       " 'aviv': <gensim.models.keyedvectors.Vocab at 0x2610b8cd320>,\n",
       " 'israel': <gensim.models.keyedvectors.Vocab at 0x2610b8cd358>,\n",
       " 'posits': <gensim.models.keyedvectors.Vocab at 0x2610b8cd390>,\n",
       " 'evolved': <gensim.models.keyedvectors.Vocab at 0x2610b8cd400>,\n",
       " 'ability': <gensim.models.keyedvectors.Vocab at 0x2610b8cd438>,\n",
       " 'differentiate': <gensim.models.keyedvectors.Vocab at 0x2610b8cd4a8>,\n",
       " 'genuinely': <gensim.models.keyedvectors.Vocab at 0x2610b8cd4e0>,\n",
       " 'reflects': <gensim.models.keyedvectors.Vocab at 0x2610b8cd518>,\n",
       " 'kind': <gensim.models.keyedvectors.Vocab at 0x2610b8cd588>,\n",
       " 'acquired': <gensim.models.keyedvectors.Vocab at 0x2610b8cd5c0>,\n",
       " 'sometimes': <gensim.models.keyedvectors.Vocab at 0x2610b8cd5f8>,\n",
       " 'theoretically': <gensim.models.keyedvectors.Vocab at 0x2610b8cd630>,\n",
       " 'least': <gensim.models.keyedvectors.Vocab at 0x2610b8cd668>,\n",
       " 'methods': <gensim.models.keyedvectors.Vocab at 0x2610b8cd6a0>,\n",
       " 'answer': <gensim.models.keyedvectors.Vocab at 0x2610b8cd6d8>,\n",
       " 'isolate': <gensim.models.keyedvectors.Vocab at 0x2610b8cd748>,\n",
       " 'gene': <gensim.models.keyedvectors.Vocab at 0x2610b8cd780>,\n",
       " 'responsible': <gensim.models.keyedvectors.Vocab at 0x2610b8cd7b8>,\n",
       " 'certain': <gensim.models.keyedvectors.Vocab at 0x2610b8cd7f0>,\n",
       " 'tend': <gensim.models.keyedvectors.Vocab at 0x2610b8cd828>,\n",
       " 'inherited': <gensim.models.keyedvectors.Vocab at 0x2610b8cd860>,\n",
       " 'along': <gensim.models.keyedvectors.Vocab at 0x2610b8cd898>,\n",
       " ...}"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.vocab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Save or Load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-11-04 22:06:04,007 : INFO : loading Doc2Vec object from D:\\Models\\Doc2Vec(Sci_related).bin\n",
      "2017-11-04 22:06:04,430 : INFO : loading wv recursively from D:\\Models\\Doc2Vec(Sci_related).bin.wv.* with mmap=None\n",
      "2017-11-04 22:06:04,432 : INFO : loading syn0 from D:\\Models\\Doc2Vec(Sci_related).bin.wv.syn0.npy with mmap=None\n",
      "2017-11-04 22:06:04,480 : INFO : setting ignored attribute syn0norm to None\n",
      "2017-11-04 22:06:04,482 : INFO : loading docvecs recursively from D:\\Models\\Doc2Vec(Sci_related).bin.docvecs.* with mmap=None\n",
      "2017-11-04 22:06:04,484 : INFO : loading syn1neg from D:\\Models\\Doc2Vec(Sci_related).bin.syn1neg.npy with mmap=None\n",
      "2017-11-04 22:06:04,541 : INFO : setting ignored attribute cum_table to None\n",
      "2017-11-04 22:06:04,542 : INFO : loaded D:\\Models\\Doc2Vec(Sci_related).bin\n"
     ]
    }
   ],
   "source": [
    "# model.save(\"D:\\\\Models\\\\Doc2Vec(Sci_related).bin\")\n",
    "model = gensim.models.Doc2Vec.load(\"D:\\\\Models\\\\Doc2Vec(Sci_related).bin\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Look-up for similar documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-11-04 22:06:04,760 : INFO : precomputing L2-norms of word weight vectors\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('web', 0.4793151617050171),\n",
       " ('facebook', 0.42751970887184143),\n",
       " ('broadband', 0.3999687731266022),\n",
       " ('network', 0.36445242166519165),\n",
       " ('openid', 0.3539382219314575),\n",
       " ('mail', 0.33882203698158264),\n",
       " ('wireless', 0.3299103081226349),\n",
       " ('blogs', 0.3297681212425232),\n",
       " ('edi', 0.32874196767807007),\n",
       " ('phishing', 0.3247973918914795)]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.similar_by_word(\"internet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('information', 0.4409201145172119),\n",
       " ('statistics', 0.3989573121070862),\n",
       " ('records', 0.36948490142822266),\n",
       " ('samples', 0.36515748500823975),\n",
       " ('observations', 0.3583360016345978),\n",
       " ('archives', 0.3361548185348511),\n",
       " ('measurements', 0.33312302827835083),\n",
       " ('readings', 0.3325759768486023),\n",
       " ('documents', 0.3289825916290283),\n",
       " ('timings', 0.3274507522583008)]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.similar_by_word(\"data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('programmers', 0.5002354383468628),\n",
       " ('users', 0.4927016496658325),\n",
       " ('vendors', 0.46623820066452026),\n",
       " ('designers', 0.42613762617111206),\n",
       " ('stutterers', 0.41394639015197754),\n",
       " ('ecologists', 0.4031423330307007),\n",
       " ('managers', 0.398607075214386),\n",
       " ('companies', 0.38800886273384094),\n",
       " ('manufacturers', 0.38262853026390076),\n",
       " ('advocates', 0.3826046884059906)]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.similar_by_word(\"developers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(' Fever Flow', 0.3851379156112671),\n",
       " (' Solar System Mysteries', 0.3793867230415344),\n",
       " (' Miniplanet sports a megapeak', 0.37395384907722473),\n",
       " (' Studying Biodiversity: Is a New Paradigm Really Needed? ',\n",
       "  0.3729775547981262),\n",
       " (' Planets common as stars in galaxy', 0.372067391872406),\n",
       " (' Planck studies the microwave galaxy', 0.3694920241832733),\n",
       " (' Data Recovery', 0.3680240511894226),\n",
       " (' BENCHED SCIENCE.', 0.3646535873413086),\n",
       " (' Exoplanets make pictorial debut', 0.3609537184238434),\n",
       " (' When Science Turns Suspect The Office of Research Integrity--a.k.a., the Fraud Squad--is on the case.',\n",
       "  0.3602791726589203)]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector = model.infer_vector(\"\"\"data science\"\"\".split())\n",
    "model.docvecs.most_similar([vector]) # similar docs & accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'D:\\\\COCA & GloWbE\\\\COCA\\\\Pre-processed Text\\\\Science-related Texts\\\\ A new language will improve online applications Cloud Programming'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-124-703e4e7bbfe0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfile_names\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0mtext\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"D:\\\\COCA & GloWbE\\\\COCA\\\\Pre-processed Text\\\\Science-related Texts\\\\\"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'r'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0mtext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'D:\\\\COCA & GloWbE\\\\COCA\\\\Pre-processed Text\\\\Science-related Texts\\\\ A new language will improve online applications Cloud Programming'"
     ]
    }
   ],
   "source": [
    "similar_docs = model.docvecs.most_similar([vector])\n",
    "file_names = [i[0] for i in similar_docs] # slice the first elements\n",
    "results = []\n",
    "        \n",
    "for name in file_names:\n",
    "    text = open(\"D:\\\\COCA & GloWbE\\\\COCA\\\\Pre-processed Text\\\\Science-related Texts\\\\\" + name, 'r')\n",
    "    results.append(text.read())\n",
    "    text.close()\n",
    "        \n",
    "i = 0\n",
    "\n",
    "for text in results:\n",
    "    print(\"Document\", i+1, \":\", file_names[i], \"\\t accuracy:\", similar_docs[i][1], \"\\n\", (text[0:500]), \"\\n\" ) \n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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