analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load analysis.py\n",
    "#!/usr/bin/python3\n",
    "\n",
    "########################################\n",
    "## import packages\n",
    "########################################\n",
    "import os\n",
    "import re\n",
    "import csv\n",
    "import logging\n",
    "import warnings\n",
    "import requests\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from operator import itemgetter\n",
    "from collections import defaultdict\n",
    "\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "\n",
    "from gensim.corpora import Dictionary, MmCorpus\n",
    "from gensim.models import LdaModel, LsiModel, TfidfModel, CoherenceModel\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pyLDAvis.gensim\n",
    "\n",
    "########################################\n",
    "## Configurations\n",
    "########################################\n",
    "\n",
    "DOCS_DIR = './docs/'\n",
    "RAILS_DOCS_DIR = DOCS_DIR + 'rails/'\n",
    "PYTHON_DOCS_DIR = DOCS_DIR + 'python/'\n",
    "STOP_WORDS = set(stopwords.words('english'))\n",
    "\n",
    "## Stackoverflow page request parameters\n",
    "pagesize = 100\n",
    "site = 'stackoverflow'\n",
    "key = 'U5pNnFT8KfJCieV9wGb5uQ(('\n",
    "wordnet_lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "#logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "########################################\n",
    "## Support Functions\n",
    "########################################\n",
    "def vectorize_docs(lang):\n",
    "    docs_vector = []\n",
    "    if lang == 'ruby':\n",
    "        with open(RAILS_DOCS_DIR+'gettingstarted.txt') as f:\n",
    "            raw = f.read().replace('\\n', '')\n",
    "            docs_vector.append(raw)\n",
    "            return docs_vector\n",
    "    elif lang == 'python':\n",
    "        python_files = [os.path.join(root, name)\n",
    "             for root, dirs, files in os.walk(PYTHON_DOCS_DIR)\n",
    "             for name in files]\n",
    "        for file in python_files:\n",
    "            with open(file) as f:\n",
    "                raw = f.read().replace('\\n', ' ')\n",
    "                docs_vector.append(raw)\n",
    "        return docs_vector\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def hasNumbers(inputString):\n",
    "    return any(char.isdigit() for char in inputString)\n",
    "\n",
    "def removeSpecialCharacters(documents):\n",
    "   return [[re.sub('[^A-Za-z0-9]+', '', str(word)) for word in document] for document in documents]\n",
    "\n",
    "def removeNumbers(documents):\n",
    "    return [[word for word in document if (len(str(word)) > 2 and not hasNumbers(str(word)))] for document in documents]\n",
    "\n",
    "def get_stackoverflow_tags():\n",
    "    if not os.path.isfile('./stackoverflow_tags.csv'):\n",
    "        has_more = True\n",
    "        tags = []\n",
    "        page = 1\n",
    "        while(has_more):\n",
    "            request = requests.get('https://api.stackexchange.com/2.2/tags',params={'pagesize' : pagesize,'site' : site, 'page' : page  ,'key' : key}, verify=True, stream=True).json()\n",
    "            if 'has_more' in request:\n",
    "                page = page + 1\n",
    "                for item in request['items']:\n",
    "                    tags.append(item['name'])\n",
    "            else:\n",
    "                has_more = False\n",
    "        with open('./stackoverflow_tags.csv', 'wb') as outfile:\n",
    "            writer = csv.writer(outfile,delimiter=';')\n",
    "            writer.writerow(tags)\n",
    "    else:\n",
    "        tags = pd.read_csv('./stackoverflow_tags.csv', header = None, delimiter = ';')\n",
    "        return tags.values.tolist()\n",
    "    return tags\n",
    "\n",
    "\n",
    "def evaluate_graph(dictionary, corpus, texts, limit):\n",
    "    c_v = []\n",
    "    lm_list = []\n",
    "    for num_topics in range(1, limit):\n",
    "        lm = LdaModel(corpus=corpus, num_topics=num_topics, id2word=dictionary, update_every=1, chunksize=100, passes=1)\n",
    "        lm_list.append(lm)\n",
    "        cm = CoherenceModel(model=lm, texts=texts, dictionary=dictionary, coherence='c_v')\n",
    "        c_v.append(cm.get_coherence())\n",
    "\n",
    "    # Show graph\n",
    "    x = range(1, limit)\n",
    "    plt.plot(x, c_v)\n",
    "    plt.xlabel(\"num_topics\")\n",
    "    plt.ylabel(\"Coherence score\")\n",
    "    plt.legend((\"c_v\"), loc='best')\n",
    "    plt.show()\n",
    "\n",
    "    return lm_list, c_v\n",
    "\n",
    "########################################\n",
    "## Pre processing\n",
    "########################################\n",
    "\n",
    "documents = vectorize_docs('python')\n",
    "\n",
    "## Stackoverflow tags pre processing\n",
    "tags = get_stackoverflow_tags()\n",
    "tags = removeSpecialCharacters(tags)\n",
    "tags = removeNumbers(tags)\n",
    "cleared_tags = tags[0]\n",
    "\n",
    "## Create tokens from documents and check if not in STOP_WORDS\n",
    "tokenized_docs = []\n",
    "tokenized_docs = [[word for word in document.lower().split() if word not in STOP_WORDS] for document in documents]\n",
    "\n",
    "## Remove special characters and numbers\n",
    "tokenized_docs = removeSpecialCharacters(tokenized_docs)\n",
    "cleared_docs = removeNumbers(tokenized_docs)\n",
    "\n",
    "## Lemmatize docs\n",
    "tokenized_docs = [[wordnet_lemmatizer.lemmatize(word) for word in document] for document in tokenized_docs]\n",
    "\n",
    "## Remove all words that are not stackoverflow tags\n",
    "cleared_docs = [[word for word in document if word in cleared_tags] for document in cleared_docs]\n",
    "\n",
    "## Remove all words that appear only once\n",
    "frequency = defaultdict(int)\n",
    "\n",
    "for document in cleared_docs:\n",
    "    for token in document:\n",
    "        frequency[token] += 1\n",
    "\n",
    "cleared_docs = [[token.lower() for token in document if frequency[token] > 1] for document in cleared_docs]\n",
    "\n",
    "trash_tokens = []\n",
    "\n",
    "for k, v in sorted(frequency.items(), key=itemgetter(1), reverse=True):\n",
    "    trash_tokens.append(k)\n",
    "\n",
    "cleared_tokens = trash_tokens[int(len(trash_tokens) * 0.10) : int(len(trash_tokens) * .90)]\n",
    "\n",
    "cleared_docs = [[token for token in document if token in cleared_tokens] for document in cleared_docs]\n",
    "\n",
    "## Save dictionary in serialized form\n",
    "dictionary = Dictionary(cleared_docs)\n",
    "dictionary.save('./dictionaries/python_tags.dict')\n",
    "corpus = [dictionary.doc2bow(document) for document in cleared_docs]\n",
    "MmCorpus.serialize('./dictionaries/python_tags.mm', corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Used dictionary generated\n"
     ]
    }
   ],
   "source": [
    "########################################\n",
    "## Load Data\n",
    "########################################\n",
    "if (os.path.exists(\"./dictionaries/python_tags.dict\")):\n",
    "    dictionary = Dictionary.load('./dictionaries/python_tags.dict')\n",
    "    corpus = MmCorpus('./dictionaries/python_tags.mm')\n",
    "    print(\"Used dictionary generated\")\n",
    "else:\n",
    "    print(\"Please run the preprocessing to generate a dictionary file\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MmCorpus(465 documents, 2752 features, 27148 non-zero entries)\n"
     ]
    }
   ],
   "source": [
    "########################################\n",
    "## Create Model\n",
    "########################################\n",
    "print(corpus)\n",
    "tfidf = TfidfModel(corpus)\n",
    "corpus_tfidf = tfidf[corpus]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "########################################\n",
    "## Applying LSI\n",
    "########################################\n",
    "lsi = LsiModel(corpus_tfidf, id2word=dictionary, num_topics=120, decay=1, onepass=False, extra_samples=20)\n",
    "corpus_lsi = lsi[corpus_tfidf]\n",
    "print(lsi.show_topics(num_topics=10))\n",
    "lsitopics = lsi.show_topics(formatted=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "########################################\n",
    "## Applying LDA\n",
    "########################################\n",
    "lda = LdaModel(corpus=corpus, id2word=dictionary, num_topics=120, update_every=1, chunksize=100, passes=1)\n",
    "print(lda.show_topics(num_topics=10))\n",
    "ldatopics = lda.show_topics(formatted=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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\"symbols\", \"symlink\", \"symmetric\", \"symmetry\", \"sync\", \"synchronization\", \"synchronization\", \"synchronization\", \"synchronization\", \"synchronization\", \"synchronize\", \"synchronized\", \"synchronized\", \"synchronized\", \"synonym\", \"synonym\", \"synonym\", \"synonym\", \"syntaxerror\", \"sys\", \"sys\", \"sys\", \"sys\", \"sys\", \"sys\", \"sys\", \"sys\", \"sys\", \"sys\", \"sys\", \"sys\", \"sys\", \"sys\", \"sys\", \"syslog\", \"syslog\", \"syslog\", \"systemerror\", \"systemtap\", \"systemtap\", \"systemtap\", \"systemtap\", \"tabs\", \"tabs\", \"tabs\", \"tabs\", \"tabs\", \"tabs\", \"tabs\", \"tabs\", \"tabs\", \"tabs\", \"tabular\", \"tabular\", \"tagname\", \"tagname\", \"tags\", \"tags\", \"tags\", \"tags\", \"tags\", \"tail\", \"tail\", \"tail\", \"tail\", \"tar\", \"tar\", \"tar\", \"tar\", \"tar\", \"tarfile\", \"tarfile\", \"tarfile\", \"targets\", \"targets\", \"targets\", \"tcl\", \"tcl\", \"tcl\", \"tcp\", \"tcp\", \"tcp\", \"tcp\", \"tcp\", \"tcp\", \"tcp\", \"tcp\", \"tcpip\", \"tcpserver\", \"teardown\", \"teardown\", \"tee\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"telnet\", \"telnet\", \"telnetlib\", \"temp\", \"templating\", \"templating\", \"temporary\", \"temporary\", \"temporary\", \"temporary\", \"temporary\", \"temporary\", \"temporary\", \"temporary\", \"temporary\", \"term\", \"term\", \"term\", \"term\", \"term\", \"terminate\", \"terminate\", \"terminate\", \"terminate\", \"terminate\", \"terminate\", \"terminate\", \"terminate\", \"terminate\", \"termination\", \"termination\", \"termination\", \"termination\", \"termination\", \"termination\", \"terminator\", \"terminator\", \"terminator\", \"terminology\", \"terminology\", \"termios\", \"testcase\", \"testcase\", \"testcase\", \"testsuite\", \"testsuite\", \"textbox\", \"textbox\", \"textbox\", \"textnode\", \"thai\", \"themes\", \"theory\", \"theory\", \"theory\", \"theory\", \"thin\", \"thin\", 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\"toplevel\", \"toplevel\", \"tostring\", \"touch\", \"trace\", \"trace\", \"trace\", \"tracing\", \"tracing\", \"tracker\", \"tracker\", \"tracker\", \"tracker\", \"tracking\", \"tracking\", \"tracking\", \"tracking\", \"tracking\", \"tracking\", \"traffic\", \"transactions\", \"transactions\", \"transactions\", \"transfer\", \"transfer\", \"transfer\", \"transfer\", \"transfer\", \"transfer\", \"transferencoding\", \"transferencoding\", \"transform\", \"transform\", \"transform\", \"transform\", \"transform\", \"transformation\", \"transformation\", \"transformation\", \"transformation\", \"transition\", \"transition\", \"transition\", \"translate\", \"translate\", \"translate\", \"translate\", \"translate\", \"translation\", \"translation\", \"translation\", \"translation\", \"translation\", \"translation\", \"translation\", \"translation\", \"translation\", \"transmission\", \"transmission\", \"transparency\", \"transport\", \"transport\", \"transport\", \"transport\", \"transport\", \"transport\", \"transport\", \"transport\", \"transport\", \"transport\", \"traversal\", \"traversal\", \"traversal\", \"traversal\", \"traversal\", \"traversal\", \"traversal\", \"treeview\", \"trial\", \"triangular\", \"triggers\", \"triggers\", \"triples\", \"triples\", \"triples\", \"trivial\", \"trivial\", \"trivial\", \"truncate\", \"truncate\", \"truncate\", \"truncate\", \"truncated\", \"truncated\", \"truncated\", \"truncated\", \"truncated\", \"truncated\", \"truncated\", \"trust\", \"trusted\", \"tryfinally\", \"tryfinally\", \"ttk\", \"ttk\", \"ttk\", \"ttk\", \"ttk\", \"tty\", \"turn\", \"turn\", \"turn\", \"turn\", \"turn\", \"turn\", \"turn\", \"turn\", \"turn\", \"turn\", \"turn\", \"turn\", \"turn\", \"turn\", \"turn\", \"turn\", \"twisted\", \"twisted\", \"typechecking\", \"typechecking\", \"typed\", \"typed\", \"typed\", \"typed\", \"typed\", \"typed\", \"typed\", \"typeid\", \"typing\", \"typing\", \"typing\", \"typing\", \"typing\", \"typing\", \"typing\", \"typing\", \"typing\", \"typing\", \"tzinfo\", \"tzinfo\", \"tzinfo\", \"tzinfo\", \"tzinfo\", \"tzinfo\", \"udp\", \"udp\", \"udp\", \"udp\", \"uid\", \"uid\", \"uid\", \"umask\", \"unbuffered\", \"undefined\", \"undefined\", \"undefined\", \"undefined\", \"undefined\", \"undefined\", \"undefined\", \"undefined\", \"undefined\", \"undefined\", \"underflow\", \"underline\", \"underline\", \"underline\", \"underline\", \"underscores\", \"underscores\", \"underscores\", \"underscores\", \"underscores\", \"undo\", \"undo\", \"undo\", \"unhandled\", \"unhandled\", \"unhandled\", \"unicodeliterals\", \"unification\", \"uniform\", \"uniform\", \"uninstall\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"unions\", \"unions\", \"unions\", \"unions\", \"unions\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"unique\", \"universal\", \"universal\", \"universal\", \"universal\", \"universal\", \"universal\", \"unlink\", \"unlink\", \"unlock\", \"unordered\", \"unordered\", \"unsafe\", \"unsafe\", \"unset\", \"unsigned\", \"unsigned\", \"unsigned\", \"unsigned\", \"unsigned\", \"unsigned\", \"unsigned\", \"unsigned\", \"unsigned\", \"unspecified\", \"unspecified\", \"unspecified\", \"unspecified\", \"unspecified\", \"unwrap\", \"unzip\", \"updates\", \"updates\", \"updates\", \"updates\", \"updates\", \"updates\", \"updates\", \"updates\", \"upgrade\", \"uppercase\", \"uppercase\", \"uppercase\", \"uppercase\", \"uppercase\", \"uppercase\", \"uppercase\", \"uppercase\", \"uppercase\", \"uptodate\", \"uri\", \"uri\", \"uri\", \"uri\", \"uri\", \"uri\", \"uri\", \"uri\", \"uri\", \"uri\", \"urllib\", \"urllib\", \"urllib\", \"urllib\", \"urllib\", \"urllib\", \"urllib\", \"urllib\", \"urllib\", \"urlopen\", \"urlopen\", \"urlparse\", \"useragent\", \"useragent\", \"userdefined\", \"userdefined\", \"userdefined\", \"userdefined\", \"userdefined\", \"userdefined\", \"userdefined\", \"userdefined\", \"userdefined\", \"userfriendly\", \"userid\", \"userid\", \"username\", \"username\", \"username\", \"username\", \"utc\", \"utc\", \"utc\", \"utc\", \"utc\", \"utc\", \"utilities\", \"utilities\", \"utilities\", \"utilities\", \"utilities\", \"utilities\", \"utilities\", \"utilities\", \"utilities\", \"utilities\", \"utilities\", \"utilities\", \"utility\", \"utility\", \"utility\", \"utility\", \"utility\", \"utility\", \"utility\", \"utility\", \"utility\", \"utility\", \"utility\", \"utility\", \"utility\", \"utility\", \"utility\", \"utility\", \"utility\", \"uuencode\", \"uuid\", \"validating\", \"validating\", \"validation\", \"var\", \"var\", \"variablelength\", \"variance\", \"variance\", \"variant\", \"variant\", \"variant\", \"variant\", \"variant\", \"variant\", \"variant\", \"variant\", \"variants\", \"variants\", 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      "text/plain": [
       "PreparedData(topic_coordinates=           Freq  cluster  topics         x         y\n",
       "topic                                               \n",
       "67     5.855024        1       1  0.139536  0.310638\n",
       "1      4.300858        1       2  0.123682 -0.006078\n",
       "73     3.784855        1       3  0.123277  0.170848\n",
       "107    3.657101        1       4  0.115783  0.211429\n",
       "104    3.383974        1       5  0.092626 -0.028873\n",
       "102    3.365378        1       6  0.166387  0.036644\n",
       "117    2.901602        1       7  0.104267  0.266126\n",
       "66     2.884021        1       8  0.173497  0.120344\n",
       "41     2.456098        1       9  0.135013 -0.040911\n",
       "14     2.183784        1      10  0.104472 -0.021846\n",
       "84     2.127613        1      11  0.136388  0.014857\n",
       "29     2.097593        1      12  0.135819  0.184976\n",
       "58     2.064709        1      13  0.163104 -0.017020\n",
       "79     2.034298        1      14  0.166302  0.008064\n",
       "8      1.897166        1      15  0.151182  0.106572\n",
       "89     1.866773        1      16  0.110745  0.008960\n",
       "53     1.820725        1      17  0.108920  0.122468\n",
       "57     1.812289        1      18  0.090975 -0.041873\n",
       "95     1.810696        1      19  0.134631  0.009627\n",
       "30     1.661113        1      20  0.113769 -0.053213\n",
       "115    1.577683        1      21  0.116283  0.034828\n",
       "62     1.527885        1      22  0.122972 -0.025638\n",
       "35     1.514517        1      23  0.165664 -0.038351\n",
       "77     1.441053        1      24  0.118005  0.045553\n",
       "92     1.384062        1      25  0.107773 -0.052453\n",
       "60     1.316856        1      26  0.070530  0.211316\n",
       "88     1.312478        1      27  0.119294 -0.035421\n",
       "56     1.302204        1      28  0.137838  0.020927\n",
       "15     1.210081        1      29  0.106024  0.037759\n",
       "17     1.206044        1      30  0.180476 -0.090422\n",
       "...         ...      ...     ...       ...       ...\n",
       "32     0.084536        1      91 -0.147938  0.084117\n",
       "80     0.026588        1      92 -0.168263  0.001195\n",
       "22     0.022437        1      93 -0.200928 -0.018883\n",
       "10     0.006481        1      94 -0.185020 -0.002680\n",
       "12     0.006481        1      95 -0.189468  0.000825\n",
       "13     0.006481        1      96 -0.185506  0.002674\n",
       "21     0.006481        1      97 -0.199643 -0.006060\n",
       "59     0.006481        1      98 -0.193408 -0.005680\n",
       "23     0.006481        1      99 -0.199115 -0.005553\n",
       "25     0.006481        1     100 -0.190438 -0.004690\n",
       "112    0.006481        1     101 -0.188117  0.001194\n",
       "111    0.006481        1     102 -0.189779 -0.003439\n",
       "109    0.006481        1     103 -0.190511 -0.000448\n",
       "105    0.006481        1     104 -0.191053 -0.001802\n",
       "96     0.006481        1     105 -0.187151  0.056140\n",
       "91     0.006481        1     106 -0.171077  0.062095\n",
       "85     0.006481        1     107 -0.162825  0.045820\n",
       "83     0.006481        1     108 -0.170609  0.009553\n",
       "78     0.006481        1     109 -0.198738 -0.006034\n",
       "74     0.006481        1     110 -0.162595  0.114497\n",
       "69     0.006481        1     111 -0.172244  0.038064\n",
       "68     0.006481        1     112 -0.170949  0.004477\n",
       "65     0.006481        1     113 -0.187263 -0.003042\n",
       "64     0.006481        1     114 -0.179602  0.009251\n",
       "63     0.006481        1     115 -0.194960 -0.005875\n",
       "49     0.006481        1     116 -0.174725  0.070741\n",
       "38     0.006481        1     117 -0.161325  0.042762\n",
       "34     0.006481        1     118 -0.166180  0.070658\n",
       "31     0.006481        1     119 -0.194745 -0.005972\n",
       "0      0.006481        1     120 -0.169378  0.035391\n",
       "\n",
       "[120 rows x 5 columns], topic_info=      Category        Freq             Term       Total  loglift  logprob\n",
       "term                                                                     \n",
       "117    Default  209.000000          console  209.000000  30.0000  30.0000\n",
       "666    Default  200.000000           clause  200.000000  29.0000  29.0000\n",
       "571    Default  164.000000              tar  164.000000  28.0000  28.0000\n",
       "123    Default  140.000000        converter  140.000000  27.0000  27.0000\n",
       "958    Default  138.000000             func  138.000000  26.0000  26.0000\n",
       "48     Default  139.000000              any  139.000000  25.0000  25.0000\n",
       "1327   Default  153.000000          subtype  153.000000  24.0000  24.0000\n",
       "32     Default  181.000000              web  181.000000  23.0000  23.0000\n",
       "366    Default  190.000000         metadata  190.000000  22.0000  22.0000\n",
       "124    Default  154.000000       converters  154.000000  21.0000  21.0000\n",
       "636    Default  183.000000             weak  183.000000  20.0000  20.0000\n",
       "223    Default  182.000000           finder  182.000000  19.0000  19.0000\n",
       "810    Default  140.000000           strict  140.000000  18.0000  18.0000\n",
       "24     Default  164.000000           select  164.000000  17.0000  17.0000\n",
       "574    Default  123.000000             tell  123.000000  16.0000  16.0000\n",
       "218    Default  189.000000        filenames  189.000000  15.0000  15.0000\n",
       "793    Default  115.000000            owner  115.000000  14.0000  14.0000\n",
       "906    Default   98.000000         keyerror   98.000000  13.0000  13.0000\n",
       "596    Default  151.000000            trace  151.000000  12.0000  12.0000\n",
       "296    Default  175.000000        installer  175.000000  11.0000  11.0000\n",
       "788    Default  137.000000             mean  137.000000  10.0000  10.0000\n",
       "63     Default  148.000000        backslash  148.000000   9.0000   9.0000\n",
       "289    Default  242.000000        inherited  242.000000   8.0000   8.0000\n",
       "756    Default  139.000000           author  139.000000   7.0000   7.0000\n",
       "650    Default  121.000000           xmlrpc  121.000000   6.0000   6.0000\n",
       "679    Default  134.000000      declaration  134.000000   5.0000   5.0000\n",
       "946    Default  137.000000            crash  137.000000   4.0000   4.0000\n",
       "259    Default  119.000000             heap  119.000000   3.0000   3.0000\n",
       "351    Default  102.000000             mask  102.000000   2.0000   2.0000\n",
       "566    Default  155.000000              sys  155.000000   1.0000   1.0000\n",
       "...        ...         ...              ...         ...      ...      ...\n",
       "1259  Topic120    0.006324            chars    9.315130   2.3491  -6.4597\n",
       "898   Topic120    0.010293         stringio   18.508522   2.1496  -5.9726\n",
       "1580  Topic120    0.009735     registration   17.898884   2.1273  -6.0284\n",
       "1373  Topic120    0.001725         exitcode    1.889030   2.6453  -7.7591\n",
       "12    Topic120    0.046111            enter  146.063416   1.5834  -4.4730\n",
       "888   Topic120    0.021116         suppress   52.762058   1.8206  -5.2541\n",
       "605   Topic120    0.010102       tryfinally   21.877478   1.9637  -5.9913\n",
       "1135  Topic120    0.011543       validation   29.621840   1.7939  -5.8580\n",
       "1291  Topic120    0.006133             lens   11.534395   2.1047  -6.4904\n",
       "484   Topic120    0.024796        resources  113.931885   1.2114  -5.0934\n",
       "1324  Topic120    0.006482      subsequence   14.159855   1.9550  -6.4350\n",
       "621   Topic120    0.014942        utilities   58.881260   1.3650  -5.5999\n",
       "638   Topic120    0.005265    withstatement   11.007214   1.9988  -6.6430\n",
       "1581  Topic120    0.004278           review    7.751822   2.1420  -6.8505\n",
       "1035  Topic120    0.017033           stdout  111.822289   0.8546  -5.4689\n",
       "791   Topic120    0.015568           notice   98.672554   0.8897  -5.5589\n",
       "3     Topic120    0.012204         reserved   63.517414   1.0868  -5.8024\n",
       "1389  Topic120    0.008430            cover   30.836197   1.4395  -6.1723\n",
       "1194  Topic120    0.010446             pipe   58.292759   1.0171  -5.9579\n",
       "1294  Topic120    0.009385        lowercase   56.829712   0.9354  -6.0649\n",
       "487   Topic120    0.011208          reverse   99.814911   0.5496  -5.8875\n",
       "507   Topic120    0.010581        separator   88.684502   0.6103  -5.9450\n",
       "666   Topic120    0.012855           clause  200.391373  -0.0102  -5.7504\n",
       "1337  Topic120    0.008905        uppercase   50.074841   1.0094  -6.1175\n",
       "48    Topic120    0.010092              any  139.841873   0.1076  -5.9923\n",
       "383   Topic120    0.009541  multiprocessing  106.664673   0.3222  -6.0485\n",
       "628   Topic120    0.008831             view  105.197113   0.2588  -6.1258\n",
       "432   Topic120    0.008376             pool   70.811790   0.6016  -6.1788\n",
       "361   Topic120    0.008112       memoryview   81.384933   0.4306  -6.2107\n",
       "670   Topic120    0.007731        construct   94.066490   0.2375  -6.2589\n",
       "\n",
       "[6033 rows x 6 columns], token_table=      Topic      Freq          Term\n",
       "term                               \n",
       "1596     16  0.654635  abbreviation\n",
       "1596     24  0.163659  abbreviation\n",
       "33        4  0.029776           abi\n",
       "33        5  0.744401           abi\n",
       "33       20  0.178656           abi\n",
       "1039     11  0.894885         abort\n",
       "1039     36  0.027965         abort\n",
       "1039     68  0.055930         abort\n",
       "656       1  0.197214    accessible\n",
       "656       2  0.061629    accessible\n",
       "656       4  0.123259    accessible\n",
       "656       8  0.308147    accessible\n",
       "656      12  0.073955    accessible\n",
       "656      17  0.110933    accessible\n",
       "656      19  0.012326    accessible\n",
       "656      20  0.012326    accessible\n",
       "656      26  0.036978    accessible\n",
       "656      31  0.012326    accessible\n",
       "656      37  0.012326    accessible\n",
       "656      45  0.012326    accessible\n",
       "34        6  0.070239      accessor\n",
       "34       39  0.140478      accessor\n",
       "34       45  0.632152      accessor\n",
       "34       47  0.070239      accessor\n",
       "5         3  0.098001       account\n",
       "5         9  0.016334       account\n",
       "5        10  0.081668       account\n",
       "5        12  0.522675       account\n",
       "5        19  0.016334       account\n",
       "5        30  0.179669       account\n",
       "...     ...       ...           ...\n",
       "649       1  0.095875     xmlreader\n",
       "649      58  0.767002     xmlreader\n",
       "650       2  0.008261        xmlrpc\n",
       "650       5  0.008261        xmlrpc\n",
       "650      10  0.859142        xmlrpc\n",
       "650      83  0.099132        xmlrpc\n",
       "651      10  0.809948  xmlrpcclient\n",
       "651      83  0.161990  xmlrpcclient\n",
       "1779      3  0.942340           xor\n",
       "652      45  0.453899         xpath\n",
       "1652     16  0.766743         xterm\n",
       "1395     20  0.831733     yieldfrom\n",
       "653       4  0.790174        zeromq\n",
       "653       5  0.131696        zeromq\n",
       "654      21  0.010204       zipfile\n",
       "654      24  0.969358       zipfile\n",
       "655       5  0.025757          zlib\n",
       "655      24  0.077271          zlib\n",
       "655      25  0.128784          zlib\n",
       "655      55  0.592408          zlib\n",
       "655      67  0.025757          zlib\n",
       "655      82  0.025757          zlib\n",
       "655      93  0.077271          zlib\n",
       "885      10  0.687955          zone\n",
       "885      22  0.076439          zone\n",
       "885      73  0.076439          zone\n",
       "886      10  0.764546      zoneinfo\n",
       "818      10  0.430491         zones\n",
       "819      25  0.843484          zope\n",
       "819      67  0.076680          zope\n",
       "\n",
       "[6259 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[68, 2, 74, 108, 105, 103, 118, 67, 42, 15, 85, 30, 59, 80, 9, 90, 54, 58, 96, 31, 116, 63, 36, 78, 93, 61, 89, 57, 16, 18, 41, 28, 37, 109, 19, 21, 51, 76, 82, 27, 46, 6, 17, 72, 55, 99, 52, 25, 48, 114, 87, 20, 100, 101, 117, 38, 29, 107, 111, 8, 10, 56, 83, 95, 45, 71, 3, 34, 104, 73, 7, 53, 88, 94, 120, 77, 115, 91, 62, 4, 47, 40, 12, 5, 44, 119, 43, 98, 49, 102, 33, 81, 23, 11, 13, 14, 22, 60, 24, 26, 113, 112, 110, 106, 97, 92, 86, 84, 79, 75, 70, 69, 66, 65, 64, 50, 39, 35, 32, 1])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "########################################\n",
    "## Generate Intertopic Distance Map\n",
    "########################################\n",
    "pyLDAvis.enable_notebook()\n",
    "pyLDAvis.gensim.prepare(lda, corpus, dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff32580e9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "########################################\n",
    "## Generate Topic Coherence Graph\n",
    "########################################\n",
    "lmlist, c_v = evaluate_graph(dictionary=dictionary, corpus=corpus, texts=cleared_docs, limit=50)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}