gensim_news_classification.ipynb

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    "# News classification with topic models in gensim\n",
    "News article classification is a task which is performed on a huge scale by news agencies all over the world. We will be looking into how topic modeling can be used to accurately classify news articles into different categories such as sports, technology, politics etc.\n",
    "\n",
    "Our aim in this tutorial is to come up with some topic model which can come up with topics that can easily be interpreted by us. Such a topic model can be used to discover hidden structure in the corpus and can also be used to determine the membership of a news article into one of the topics.\n",
    "\n",
    "For this tutorial, we will be using the Lee corpus which is a shortened version of the [Lee Background Corpus](http://www.socsci.uci.edu/~mdlee/lee_pincombe_welsh_document.PDF). The shortened version consists of 300 documents selected from the Australian Broadcasting Corporation's news mail service. It consists of texts of headline stories from around the year 2000-2001.\n",
    "\n",
    "Accompanying slides can be found [here](https://speakerdeck.com/dsquareindia/pycon-delhi-lightening).\n",
    "\n",
    "### Requirements\n",
    "In this tutorial we look at how different topic models can be easily created using [gensim](https://radimrehurek.com/gensim/).\n",
    "Following are the dependencies for this tutorial:\n",
    "    - Gensim Version >=0.13.1 would be preferred since we will be using topic coherence metrics extensively here.\n",
    "    - matplotlib\n",
    "    - nltk.stopwords and nltk.wordnet\n",
    "    - pyLDAVis"
   ]
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   "source": [
    "import os\n",
    "import re\n",
    "import operator\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "import gensim\n",
    "import numpy as np\n",
    "warnings.filterwarnings('ignore')  # Let's not pay heed to them right now\n",
    "\n",
    "import logging\n",
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG)\n",
    "\n",
    "import nltk\n",
    "\n",
    "from gensim.models import CoherenceModel, LdaModel\n",
    "from gensim.corpora import Dictionary\n",
    "from pprint import pprint\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import load_lee_background_corpus as load_texts"
   ]
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    "Analysing our corpus.\n",
    "    - The first document talks about a bushfire that had occured in New South Wales.\n",
    "    - The second talks about conflict between India and Pakistan in Kashmir.\n",
    "    - The third talks about road accidents in the New South Wales area.\n",
    "    - The fourth one talks about Argentina's economic and political crisis during that time.\n",
    "    - The last one talks about the use of drugs by midwives in a Sydney hospital.\n",
    "Our final topic model should be giving us keywords which we can easily interpret and make a small summary out of. Without this the topic model cannot be of much practical use."
   ]
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      "/Users/home/Desenvolvimento/anaconda3/lib/python3.6/site-packages/gensim/test/test_data\n",
      "['Hundreds of people have been forced to vacate their homes in the Southern Highlands of New South Wales as strong winds today pushed a huge bushfire towards the town of Hill Top. A new blaze near Goulburn, south-west of Sydney, has forced the closure of the Hume Highway. At about 4:00pm AEDT, a marked deterioration in the weather as a storm cell moved east across the Blue Mountains forced authorities to make a decision to evacuate people from homes in outlying streets at Hill Top in the New South Wales southern highlands. An estimated 500 residents have left their homes for nearby Mittagong. The New South Wales Rural Fire Service says the weather conditions which caused the fire to burn in a finger formation have now eased and about 60 fire units in and around Hill Top are optimistic of defending all properties. As more than 100 blazes burn on New Year\\'s Eve in New South Wales, fire crews have been called to new fire at Gunning, south of Goulburn. While few details are available at this stage, fire authorities says it has closed the Hume Highway in both directions. Meanwhile, a new fire in Sydney\\'s west is no longer threatening properties in the Cranebrook area. Rain has fallen in some parts of the Illawarra, Sydney, the Hunter Valley and the north coast. But the Bureau of Meteorology\\'s Claire Richards says the rain has done little to ease any of the hundred fires still burning across the state. \"The falls have been quite isolated in those areas and generally the falls have been less than about five millimetres,\" she said. \"In some places really not significant at all, less than a millimetre, so there hasn\\'t been much relief as far as rain is concerned. \"In fact, they\\'ve probably hampered the efforts of the firefighters more because of the wind gusts that are associated with those thunderstorms.\" \\n']\n",
      "[\"Indian security forces have shot dead eight suspected militants in a night-long encounter in southern Kashmir. The shootout took place at Dora village some 50 kilometers south of the Kashmiri summer capital Srinagar. The deaths came as Pakistani police arrested more than two dozen militants from extremist groups accused of staging an attack on India's parliament. India has accused Pakistan-based Lashkar-e-Taiba and Jaish-e-Mohammad of carrying out the attack on December 13 at the behest of Pakistani military intelligence. Military tensions have soared since the raid, with both sides massing troops along their border and trading tit-for-tat diplomatic sanctions. Yesterday, Pakistan announced it had arrested Lashkar-e-Taiba chief Hafiz Mohammed Saeed. Police in Karachi say it is likely more raids will be launched against the two groups as well as other militant organisations accused of targetting India. Military tensions between India and Pakistan have escalated to a level not seen since their 1971 war. \\n\"]\n",
      "['The national road toll for the Christmas-New Year holiday period stands at 45, eight fewer than for the same time last year. 20 people have died on New South Wales roads, with eight fatalities in both Queensland and Victoria. Western Australia, the Northern Territory and South Australia have each recorded three deaths, while the ACT and Tasmania remain fatality free. \\n']\n",
      "[\"Argentina's political and economic crisis has deepened with the resignation of its interim President who took office just a week ago. Aldolfo Rodregiuez Saa told a stunned nation that he could not rescue Argentina because key fellow Peronists would not support his default on massive foreign debt repayment or his plan for a new currency. It was only a week ago that he was promising a million new jobs to end four years of recession, days after his predecessor resigned following a series of failed rescue packages. After announcing that the senate leader, Ramon Puerta, would assume the presidency until congress appoints a new caretaker president, the government said he too had quit and another senior lawmaker would act in the role. Fresh elections are not scheduled until March leaving whoever assumes the presidency with the daunting task of tackling Argentina's worst crisis in 12 years, but this time, isolated by international lending agencies. \\n\"]\n",
      "['Six midwives have been suspended at Wollongong Hospital, south of Sydney, for inappropriate use of nitrous oxide during work hours, on some occasions while women were in labour. The Illawarra Area Health Service says that following an investigation of unprofessional conduct, a further four midwives have been relocated to other areas within the hospital. The service\\'s chief executive officer, Tony Sherbon, says no one was put at risk, because other staff not involved in the use of nitrous oxide were able to take over caring for women in labour. \"Well we\\'re very concerned and the body of midwives to the hospital - there are over 70 midwives that work in our service - are very annoyed and angry at the inappropriate behaviour of these very senior people who should know better,\" he said. \"And that\\'s why we\\'ve take the action of suspending them and we\\'ll consider further action next week.\" \\n']\n",
      "['The Federal Government says it should be safe for Afghani asylum seekers in Australia to return home when the environment becomes secure. The Government has suspended their applications while the interim government is established in Kabul. The Foreign Affairs Minister Alexander Downer has refused to say for how long the claims process has been put on hold. But he says the major threat to most people seeking asylum is no longer there. \"Many Afghans who have tried to get into Australia or for that matter into Britain and other countries in north-west Europe have claimed that they are fleeing the Taliban,\" he said. \"Well, the Taliban is no longer in power in Afghanistan, the Taliban is finished.\" Meanwhile, there has been a mass airlift of detainees from Christmas Island to the Pacific Island of Nauru. In total, more than 300 people have been flown from the island in two operations using chartered aircraft. The second airlift today delivered 180 asylum seekers to Nauru where they will await processing of their claims for temporary visas. The Department of Immigration says there are now 211 detainees remaining on Christmas Island. A spokesman says a decision regarding their future is yet to be made. \\n']\n"
     ]
    }
   ],
   "source": [
    "lee_train_file = load_texts.get_lee_train_file()\n",
    "with open(lee_train_file) as f:\n",
    "    for n, l in enumerate(f):\n",
    "        if n < 6:\n",
    "            print([l])"
   ]
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    "### Preprocessing our data. Remember: Garbage In Garbage Out\n",
    "                                        \"NLP is 80% preprocessing.\"\n",
    "                                                                -Lev Konstantinovskiy\n",
    "This is the single most important step in setting up a good topic modeling system. If the preprocessing is not good, the algorithm can't do much since we would be feeding it a lot of noise. In this tutorial, we will be filtering out the noise using the following steps in this order for each line:\n",
    "1. Stopword removal using NLTK's english stopwords dataset.\n",
    "2. Bigram collocation detection (frequently co-occuring tokens) using gensim's [Phrases](https://radimrehurek.com/gensim/models/phrases.html). This is our first attempt to find some hidden structure in the corpus. You can even try trigram collocation detection.\n",
    "3. Lemmatization (using gensim's [`lemmatize`](https://radimrehurek.com/gensim/utils.html#gensim.utils.lemmatize)) to only keep the nouns. Lemmatization is generally better than stemming in the case of topic modeling since the words after lemmatization still remain understable. However, generally stemming might be preferred if the data is being fed into a vectorizer and isn't intended to be viewed."
   ]
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      "/Users/home/Desenvolvimento/anaconda3/lib/python3.6/site-packages/gensim/test/test_data\n",
      "The Federal Government says it should be safe for Afghani asylum seekers in Australia to return home when the environment becomes secure. The Government has suspended their applications while the interim government is established in Kabul. The Foreign Affairs Minister Alexander Downer has refused to say for how long the claims process has been put on hold. But he says the major threat to most people seeking asylum is no longer there. \"Many Afghans who have tried to get into Australia or for that matter into Britain and other countries in north-west Europe have claimed that they are fleeing the Taliban,\" he said. \"Well, the Taliban is no longer in power in Afghanistan, the Taliban is finished.\" Meanwhile, there has been a mass airlift of detainees from Christmas Island to the Pacific Island of Nauru. In total, more than 300 people have been flown from the island in two operations using chartered aircraft. The second airlift today delivered 180 asylum seekers to Nauru where they will await processing of their claims for temporary visas. The Department of Immigration says there are now 211 detainees remaining on Christmas Island. A spokesman says a decision regarding their future is yet to be made. \n",
      "\n"
     ]
    }
   ],
   "source": [
    "texts = load_texts.get_raw_texts()\n",
    "print(texts[5])"
   ]
  },
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   "execution_count": 4,
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     "name": "stderr",
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     "text": [
      "2017-11-22 01:39:32,884 : INFO : collecting all words and their counts\n",
      "2017-11-22 01:39:32,887 : INFO : PROGRESS: at sentence #0, processed 0 words and 0 word types\n",
      "2017-11-22 01:39:32,977 : INFO : collected 39691 word types from a corpus of 48449 words (unigram + bigrams) and 300 sentences\n",
      "2017-11-22 01:39:32,979 : INFO : using 39691 counts as vocab in Phrases<0 vocab, min_count=5, threshold=10.0, max_vocab_size=40000000>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['federal_government', 'say', 'safe', 'afghani', 'asylum_seekers', 'australia', 'return', 'home', 'environment', 'becomes', 'secure', 'government', 'suspended', 'application', 'interim_government', 'established', 'kabul', 'foreign_affairs', 'minister_alexander', 'downer', 'refused', 'say', 'long', 'claim', 'process', 'has_been', 'put', 'hold', 'say', 'major', 'threat', 'people', 'seeking', 'asylum', 'longer', 'many', 'afghan', 'tried', 'get', 'australia', 'matter', 'britain', 'country', 'north', 'west', 'europe', 'claimed', 'they_are', 'fleeing', 'taliban', 'said', 'well', 'taliban', 'longer', 'power', 'afghanistan', 'taliban', 'finished', 'meanwhile', 'has_been', 'mass', 'airlift', 'detainee', 'christmas', 'island', 'pacific', 'island', 'nauru', 'total', 'more_than', 'people', 'have_been', 'flown', 'island', 'two', 'operation', 'using', 'chartered', 'aircraft', 'second', 'airlift', 'today', 'delivered', 'asylum_seekers', 'nauru', 'await', 'processing', 'claim', 'temporary', 'visa', 'department', 'immigration', 'say', 'there_are', 'detainee', 'remaining', 'christmas', 'island', 'spokesman', 'say', 'decision', 'regarding', 'future', 'yet', 'made']\n"
     ]
    }
   ],
   "source": [
    "texts = load_texts.process_file(lee_train_file, keep_all=True)\n",
    "print(texts[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-11-22 01:40:24,435 : INFO : collecting all words and their counts\n",
      "2017-11-22 01:40:24,436 : INFO : PROGRESS: at sentence #0, processed 0 words and 0 word types\n",
      "2017-11-22 01:40:24,479 : INFO : collected 20429 word types from a corpus of 19878 words (unigram + bigrams) and 300 sentences\n",
      "2017-11-22 01:40:24,481 : INFO : using 20429 counts as vocab in Phrases<0 vocab, min_count=5, threshold=10.0, max_vocab_size=40000000>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['federal_government', 'afghani', 'asylum_seekers', 'australia', 'home', 'environment', 'secure', 'government', 'application', 'government', 'kabul', 'foreign_affairs', 'minister_alexander', 'downer', 'claim', 'process', 'hold', 'threat', 'people', 'asylum', 'afghan', 'australia', 'matter', 'britain', 'country', 'europe', 'taliban', 'taliban', 'power', 'afghanistan', 'taliban', 'mass', 'airlift', 'detainee', 'christmas', 'island', 'pacific', 'island', 'nauru', 'people', 'island', 'operation', 'aircraft', 'airlift', 'today', 'asylum_seekers', 'nauru', 'processing', 'claim', 'visa', 'department', 'immigration', 'detainee', 'christmas', 'island', 'spokesman', 'decision', 'future']\n"
     ]
    }
   ],
   "source": [
    "train_texts = load_texts.process_file(lee_train_file, keep_all=False)\n",
    "print(train_texts[5])"
   ]
  },
  {
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     "id": "0e5ca1a8-9c78-412a-9ab4-a4d0be5afd34"
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   },
   "source": [
    "Finalising our dictionary and corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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     "text": [
      "2017-11-22 01:10:35,725 : INFO : adding document #0 to Dictionary(0 unique tokens: [])\n",
      "2017-11-22 01:10:35,764 : INFO : built Dictionary(4431 unique tokens: ['hundreds', 'people', 'homes', 'southern', 'highlands']...) from 300 documents (total 18861 corpus positions)\n"
     ]
    }
   ],
   "source": [
    "dictionary = Dictionary(train_texts)\n",
    "corpus = [dictionary.doc2bow(text) for text in train_texts]"
   ]
  },
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   "source": [
    "### Topic modeling using [LDA](https://www.cs.princeton.edu/~blei/papers/HoffmanBleiBach2010b.pdf)\n",
    "This is one the most popular topic modeling algorithms today. It is a generative model in that it assumes each document is a mixture of topics and in turn, each topic is a mixture of words. To understand it better you can watch [this](https://www.youtube.com/watch?v=DDq3OVp9dNA) lecture by David Blei. Let's choose 10 topics to initialize this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-11-22 01:10:35,797 : INFO : using symmetric alpha at 0.1\n",
      "2017-11-22 01:10:35,802 : INFO : using symmetric eta at 0.00022568269013766644\n",
      "2017-11-22 01:10:35,807 : INFO : using serial LDA version on this node\n",
      "2017-11-22 01:10:36,287 : INFO : running online (single-pass) LDA training, 10 topics, 1 passes over the supplied corpus of 300 documents, updating model once every 300 documents, evaluating perplexity every 300 documents, iterating 50x with a convergence threshold of 0.001000\n",
      "2017-11-22 01:10:36,289 : WARNING : too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n",
      "2017-11-22 01:10:36,919 : DEBUG : bound: at document #0\n",
      "2017-11-22 01:10:38,797 : INFO : -11.676 per-word bound, 3272.3 perplexity estimate based on a held-out corpus of 300 documents with 18861 words\n",
      "2017-11-22 01:10:38,799 : INFO : PROGRESS: pass 0, at document #300/300\n",
      "2017-11-22 01:10:38,801 : DEBUG : performing inference on a chunk of 300 documents\n",
      "2017-11-22 01:10:39,735 : DEBUG : 34/300 documents converged within 50 iterations\n",
      "2017-11-22 01:10:39,738 : DEBUG : updating topics\n",
      "2017-11-22 01:10:39,863 : INFO : topic #6 (0.100): 0.009*\"government\" + 0.008*\"afghanistan\" + 0.006*\"man\" + 0.006*\"people\" + 0.005*\"police\" + 0.005*\"laden\" + 0.005*\"forces\" + 0.005*\"argentina\" + 0.005*\"firefighters\" + 0.004*\"area\"\n",
      "2017-11-22 01:10:39,866 : INFO : topic #4 (0.100): 0.010*\"government\" + 0.006*\"australia\" + 0.006*\"people\" + 0.006*\"taliban\" + 0.006*\"fire\" + 0.005*\"afghanistan\" + 0.005*\"qantas\" + 0.005*\"arafat\" + 0.005*\"company\" + 0.005*\"today\"\n",
      "2017-11-22 01:10:39,868 : INFO : topic #7 (0.100): 0.006*\"team\" + 0.005*\"australia\" + 0.005*\"people\" + 0.005*\"year\" + 0.005*\"cent\" + 0.004*\"sydney\" + 0.004*\"south\" + 0.004*\"today\" + 0.004*\"centre\" + 0.003*\"rate\"\n",
      "2017-11-22 01:10:39,871 : INFO : topic #8 (0.100): 0.012*\"australia\" + 0.008*\"government\" + 0.006*\"year\" + 0.004*\"people\" + 0.004*\"india\" + 0.004*\"world\" + 0.003*\"united_states\" + 0.003*\"time\" + 0.003*\"arafat\" + 0.003*\"fire\"\n",
      "2017-11-22 01:10:39,873 : INFO : topic #0 (0.100): 0.008*\"government\" + 0.006*\"australia\" + 0.004*\"police\" + 0.004*\"farmers\" + 0.004*\"months\" + 0.004*\"people\" + 0.004*\"time\" + 0.004*\"officials\" + 0.003*\"pacific\" + 0.003*\"hours\"\n",
      "2017-11-22 01:10:39,875 : INFO : topic diff=5.508733, rho=1.000000\n"
     ]
    }
   ],
   "source": [
    "ldamodel = LdaModel(corpus=corpus, num_topics=10, id2word=dictionary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "672c009d-3dbc-4a1f-a789-2a0fe78729b9"
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   },
   "source": [
    "pyLDAvis is a great way to visualize an LDA model. To summarize in short, the area of the circles represent the prevelance of the topic. The length of the bars on the right represent the membership of a term in a particular topic. pyLDAvis is based on [this](http://nlp.stanford.edu/events/illvi2014/papers/sievert-illvi2014.pdf) paper."
   ]
  },
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     "id": "f7724653-52ef-41e8-aa22-6232be216b08"
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   },
   "outputs": [],
   "source": [
    "import pyLDAvis.gensim"
   ]
  },
  {
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   "execution_count": 9,
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   "source": [
    "pyLDAvis.enable_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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    "scrolled": false
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   "outputs": [
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     "output_type": "stream",
     "text": [
      "2017-11-22 01:10:40,329 : DEBUG : performing inference on a chunk of 300 documents\n",
      "2017-11-22 01:10:40,849 : DEBUG : 261/300 documents converged within 50 iterations\n",
      "/Users/home/Desenvolvimento/anaconda3/lib/python3.6/site-packages/pyLDAvis/_prepare.py:387: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  topic_term_dists = topic_term_dists.ix[topic_order]\n"
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3.94771305983182, 2.857851932689694, 3.203540414393547, 3.5253026263121017, 2.475152558161276, 3.9862303488092072, 3.168034226645413, 3.135151961465847, 2.639442441584756, 2.3786547664000794, 2.8041912362680774, 2.609865714909846, 2.4550037758737533, 2.409280443287604, 2.3886521784943673], \"Term\": [\"afghanistan\", \"test\", \"government\", \"arafat\", \"australia\", \"qantas\", \"laden\", \"hamas\", \"team\", \"tora_bora\", \"years\", \"police\", \"argentina\", \"sydney\", \"taliban\", \"radio\", \"firefighters\", \"pakistan\", \"space\", \"debt\", \"farmers\", \"sharon\", \"forces\", \"man\", \"lockett\", \"east_timor\", \"unions\", \"wales\", \"hewitt\", \"metres\", \"offence\", \"missile\", \"hijackings\", \"sherrard\", \"terminal\", \"launceston\", \"leak\", \"moussaoui\", \"owen\", \"bombardment\", \"passes\", \"tommy\", \"marines\", \"amin\", \"auditors\", \"someone\", \"contents\", \"participant\", \"section\", \"conspiracy\", \"requirements\", \"commissions\", \"demand\", 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      "text/plain": [
       "PreparedData(topic_coordinates=            Freq  cluster  topics         x         y\n",
       "topic                                                \n",
       "5      15.976778        1       1  0.074143  0.045919\n",
       "4      14.748015        1       2  0.010210 -0.004822\n",
       "2      11.522496        1       3 -0.010270 -0.041577\n",
       "8      11.322713        1       4 -0.005559  0.009409\n",
       "3      10.191580        1       5  0.006633  0.040796\n",
       "1       9.489028        1       6  0.029626 -0.061053\n",
       "9       7.750577        1       7 -0.076655  0.015267\n",
       "7       7.280897        1       8 -0.010690 -0.094129\n",
       "6       6.439583        1       9  0.065674  0.036751\n",
       "0       5.278335        1      10 -0.083113  0.053439, topic_info=     Category        Freq            Term       Total  loglift  logprob\n",
       "term                                                                   \n",
       "220   Default   80.000000     afghanistan   80.000000  30.0000  30.0000\n",
       "596   Default   42.000000            test   42.000000  29.0000  29.0000\n",
       "165   Default  107.000000      government  107.000000  28.0000  28.0000\n",
       "845   Default   69.000000          arafat   69.000000  27.0000  27.0000\n",
       "131   Default  122.000000       australia  122.000000  26.0000  26.0000\n",
       "1687  Default   28.000000          qantas   28.000000  25.0000  25.0000\n",
       "987   Default   37.000000           laden   37.000000  24.0000  24.0000\n",
       "1562  Default   27.000000           hamas   27.000000  23.0000  23.0000\n",
       "237   Default   29.000000            team   29.000000  22.0000  22.0000\n",
       "1516  Default   30.000000       tora_bora   30.000000  21.0000  21.0000\n",
       "152   Default   38.000000           years   38.000000  20.0000  20.0000\n",
       "90    Default   66.000000          police   66.000000  19.0000  19.0000\n",
       "135   Default    9.000000       argentina    9.000000  18.0000  18.0000\n",
       "17    Default   45.000000          sydney   45.000000  17.0000  17.0000\n",
       "218   Default   45.000000         taliban   45.000000  16.0000  16.0000\n",
       "1150  Default   21.000000           radio   21.000000  15.0000  15.0000\n",
       "71    Default   19.000000    firefighters   19.000000  14.0000  14.0000\n",
       "112   Default   40.000000        pakistan   40.000000  13.0000  13.0000\n",
       "2584  Default   11.000000           space   11.000000  12.0000  12.0000\n",
       "147   Default    7.000000            debt    7.000000  11.0000  11.0000\n",
       "3128  Default    4.000000         farmers    4.000000  10.0000  10.0000\n",
       "2489  Default   20.000000          sharon   20.000000   9.0000   9.0000\n",
       "76    Default   54.000000          forces   54.000000   8.0000   8.0000\n",
       "679   Default   51.000000             man   51.000000   7.0000   7.0000\n",
       "3075  Default    7.000000         lockett    7.000000   6.0000   6.0000\n",
       "1659  Default   11.000000      east_timor   11.000000   5.0000   5.0000\n",
       "2380  Default   24.000000          unions   24.000000   4.0000   4.0000\n",
       "6     Default   32.000000           wales   32.000000   3.0000   3.0000\n",
       "615   Default   10.000000          hewitt   10.000000   2.0000   2.0000\n",
       "912   Default   29.000000          metres   29.000000   1.0000   1.0000\n",
       "...       ...         ...             ...         ...      ...      ...\n",
       "3059  Topic10    1.043679       defendant    2.208717   2.1919  -6.8605\n",
       "2692  Topic10    2.946995         islands    8.513147   1.8807  -5.8225\n",
       "3037  Topic10    2.381911         verdict    6.733753   1.9023  -6.0354\n",
       "523   Topic10    2.110109    peacekeepers    6.228180   1.8592  -6.1566\n",
       "2691  Topic10    1.899483         solomon    5.362243   1.9038  -6.2617\n",
       "2803  Topic10    1.903286           sarah    5.437248   1.8919  -6.2597\n",
       "3145  Topic10    1.656272           grant    4.438695   1.9558  -6.3987\n",
       "165   Topic10    8.236043      government  107.704265   0.3707  -4.7948\n",
       "2157  Topic10    2.379472            july    9.063282   1.6042  -6.0364\n",
       "20    Topic10    2.230295         highway    8.327552   1.6241  -6.1012\n",
       "555   Topic10    4.047794          months   31.243263   0.8979  -5.5051\n",
       "572   Topic10    2.694484            boat   15.005493   1.2244  -5.9121\n",
       "1842  Topic10    2.545883            bill   13.110947   1.3026  -5.9688\n",
       "131   Topic10    5.970572       australia  122.614413  -0.0806  -5.1164\n",
       "90    Topic10    4.449958          police   66.038177   0.2442  -5.4104\n",
       "125   Topic10    3.947713            time   54.258492   0.3209  -5.5302\n",
       "513   Topic10    2.857852       agreement   21.380723   0.9291  -5.8532\n",
       "180   Topic10    3.203540           hours   32.786705   0.6158  -5.7390\n",
       "512   Topic10    3.525303       officials   46.460790   0.3629  -5.6433\n",
       "199   Topic10    2.475153  asylum_seekers   14.865630   1.1488  -5.9970\n",
       "1     Topic10    3.986230          people  114.557542  -0.4167  -5.5204\n",
       "17    Topic10    3.168034          sydney   45.589360   0.2750  -5.7502\n",
       "75    Topic10    3.135152        security   65.583256  -0.0991  -5.7606\n",
       "81    Topic10    2.639442           place   24.259834   0.7233  -5.9327\n",
       "1675  Topic10    2.378655          policy   13.832283   1.1811  -6.0368\n",
       "236   Topic10    2.804191   united_states   63.084263  -0.1718  -5.8722\n",
       "1665  Topic10    2.609866         company   51.606504  -0.0428  -5.9440\n",
       "527   Topic10    2.455004        minister   26.114912   0.5772  -6.0052\n",
       "295   Topic10    2.409280           world   44.404374   0.0275  -6.0240\n",
       "275   Topic10    2.388652             way   28.154396   0.4746  -6.0326\n",
       "\n",
       "[781 rows x 6 columns], token_table=      Topic      Freq           Term\n",
       "term                                \n",
       "1712      4  0.264322            abc\n",
       "1712      7  0.528644            abc\n",
       "2219      7  0.424603       abortion\n",
       "4005      5  0.293269       accounts\n",
       "4005      6  0.586538       accounts\n",
       "133       1  0.255578            act\n",
       "133       2  0.170385            act\n",
       "133       3  0.340771            act\n",
       "133       4  0.085193            act\n",
       "196       1  0.128711         action\n",
       "196       2  0.231680         action\n",
       "196       3  0.102969         action\n",
       "196       4  0.102969         action\n",
       "196       5  0.077227         action\n",
       "196       6  0.077227         action\n",
       "196       7  0.102969         action\n",
       "196       8  0.051484         action\n",
       "196       9  0.025742         action\n",
       "196      10  0.051484         action\n",
       "2309      1  0.307514  administrator\n",
       "2309      8  0.615027  administrator\n",
       "511       1  0.393248         afghan\n",
       "511       2  0.221202         afghan\n",
       "511       4  0.098312         afghan\n",
       "511       5  0.098312         afghan\n",
       "511       6  0.073734         afghan\n",
       "511       8  0.024578         afghan\n",
       "511       9  0.073734         afghan\n",
       "511      10  0.024578         afghan\n",
       "220       1  0.407698    afghanistan\n",
       "...     ...       ...            ...\n",
       "42        6  0.078880           year\n",
       "42        7  0.047328           year\n",
       "42        8  0.110431           year\n",
       "42        9  0.047328           year\n",
       "42       10  0.031552           year\n",
       "152       1  0.078904          years\n",
       "152       2  0.184109          years\n",
       "152       3  0.052602          years\n",
       "152       4  0.131506          years\n",
       "152       5  0.078904          years\n",
       "152       6  0.052602          years\n",
       "152       7  0.236711          years\n",
       "152       8  0.026301          years\n",
       "152       9  0.131506          years\n",
       "152      10  0.026301          years\n",
       "111       1  0.074585      yesterday\n",
       "111       2  0.248617      yesterday\n",
       "111       3  0.099447      yesterday\n",
       "111       4  0.124309      yesterday\n",
       "111       5  0.074585      yesterday\n",
       "111       6  0.099447      yesterday\n",
       "111       7  0.074585      yesterday\n",
       "111       8  0.099447      yesterday\n",
       "111       9  0.074585      yesterday\n",
       "111      10  0.024862      yesterday\n",
       "575       8  0.866511           zeus\n",
       "1911      1  0.262600       zimbabwe\n",
       "1911      4  0.525200       zimbabwe\n",
       "1911      9  0.131300       zimbabwe\n",
       "2004      2  0.413972           zone\n",
       "\n",
       "[2019 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[6, 5, 3, 9, 4, 2, 10, 8, 7, 1])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.gensim.prepare(ldamodel, corpus, dictionary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "68d38063-fb92-4d36-b3fb-f2d3cbff9eb5"
    }
   },
   "source": [
    "### Finding out the optimal number of topics\n",
    "Topic coherence in essence measures the human interpretability of a topic model. Traditionally [perplexity has been used](http://qpleple.com/perplexity-to-evaluate-topic-models/) to evaluate topic models however this does not correlate with human annotations at times. Topic coherence is another way to evaluate topic models with a much higher guarantee on human interpretability. Thus this can be used to compare different topic models among many other use-cases. Here's a short blog I wrote explaining topic coherence:\n",
    "[What is topic coherence?](https://rare-technologies.com/what-is-topic-coherence/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "45b1a641-1152-4364-ad25-62d1f8187317"
    }
   },
   "outputs": [],
   "source": [
    "def evaluate_graph(dictionary, corpus, texts, limit):\n",
    "    \"\"\"\n",
    "    Function to display num_topics - LDA graph using c_v coherence\n",
    "    \n",
    "    Parameters:\n",
    "    ----------\n",
    "    dictionary : Gensim dictionary\n",
    "    corpus : Gensim corpus\n",
    "    limit : topic limit\n",
    "    \n",
    "    Returns:\n",
    "    -------\n",
    "    lm_list : List of LDA topic models\n",
    "    c_v : Coherence values corresponding to the LDA model with respective number of topics\n",
    "    \"\"\"\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)\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "nbpresent": {
     "id": "e8936716-d06c-4cef-ae87-5c5da9a25a85"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-11-22 01:10:41,966 : INFO : using symmetric alpha at 1.0\n",
      "2017-11-22 01:10:41,970 : INFO : using symmetric eta at 0.00022568269013766644\n",
      "2017-11-22 01:10:41,974 : INFO : using serial LDA version on this node\n",
      "2017-11-22 01:10:42,030 : INFO : running online (single-pass) LDA training, 1 topics, 1 passes over the supplied corpus of 300 documents, updating model once every 300 documents, evaluating perplexity every 300 documents, iterating 50x with a convergence threshold of 0.001000\n",
      "2017-11-22 01:10:42,031 : WARNING : too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n",
      "2017-11-22 01:10:42,064 : DEBUG : bound: at document #0\n",
      "2017-11-22 01:10:43,163 : INFO : -8.697 per-word bound, 415.0 perplexity estimate based on a held-out corpus of 300 documents with 18861 words\n",
      "2017-11-22 01:10:43,164 : INFO : PROGRESS: pass 0, at document #300/300\n",
      "2017-11-22 01:10:43,166 : DEBUG : performing inference on a chunk of 300 documents\n",
      "2017-11-22 01:10:43,246 : DEBUG : 300/300 documents converged within 50 iterations\n",
      "2017-11-22 01:10:43,247 : DEBUG : updating topics\n",
      "2017-11-22 01:10:43,251 : INFO : topic #0 (1.000): 0.007*\"australia\" + 0.006*\"people\" + 0.006*\"government\" + 0.004*\"afghanistan\" + 0.004*\"arafat\" + 0.004*\"police\" + 0.003*\"security\" + 0.003*\"year\" + 0.003*\"today\" + 0.003*\"united_states\"\n",
      "2017-11-22 01:10:43,252 : INFO : topic diff=0.661126, rho=1.000000\n",
      "2017-11-22 01:10:43,255 : DEBUG : Setting topics to those of the model: LdaModel(num_terms=4431, num_topics=1, decay=0.5, chunksize=2000)\n",
      "2017-11-22 01:10:43,259 : INFO : using ParallelWordOccurrenceAccumulator(processes=3, batch_size=64) to estimate probabilities from sliding windows\n",
      "2017-11-22 01:10:43,326 : DEBUG : completed batch 0; 64 documents processed (92 virtual)\n",
      "2017-11-22 01:10:43,354 : DEBUG : completed batch 0; 64 documents processed (271 virtual)\n",
      "2017-11-22 01:10:43,357 : DEBUG : completed batch 0; 64 documents processed (337 virtual)\n",
      "2017-11-22 01:10:43,372 : DEBUG : completed batch 1; 128 documents processed (407 virtual)\n",
      "2017-11-22 01:10:43,375 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:10:43,377 : DEBUG : completed batch 1; 92 documents processed (362 virtual)\n",
      "2017-11-22 01:10:43,379 : DEBUG : finished all batches; 128 documents processed (407 virtual)\n",
      "2017-11-22 01:10:43,381 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:10:43,382 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:10:43,384 : DEBUG : finished all batches; 92 documents processed (362 virtual)\n",
      "2017-11-22 01:10:43,386 : INFO : accumulator serialized\n",
      "2017-11-22 01:10:43,387 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:10:43,391 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:10:43,392 : INFO : accumulator serialized\n",
      "2017-11-22 01:10:43,398 : DEBUG : finished all batches; 64 documents processed (337 virtual)\n",
      "2017-11-22 01:10:43,402 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:10:43,407 : INFO : accumulator serialized\n",
      "2017-11-22 01:10:43,444 : INFO : 3 accumulators retrieved from output queue\n",
      "2017-11-22 01:10:43,463 : INFO : accumulated word occurrence stats for 1106 virtual documents\n",
      "2017-11-22 01:10:43,560 : INFO : using symmetric alpha at 0.5\n",
      "2017-11-22 01:10:43,563 : INFO : using symmetric eta at 0.00022568269013766644\n",
      "2017-11-22 01:10:43,566 : INFO : using serial LDA version on this node\n",
      "2017-11-22 01:10:43,676 : INFO : running online (single-pass) LDA training, 2 topics, 1 passes over the supplied corpus of 300 documents, updating model once every 300 documents, evaluating perplexity every 300 documents, iterating 50x with a convergence threshold of 0.001000\n",
      "2017-11-22 01:10:43,677 : WARNING : too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n",
      "2017-11-22 01:10:43,780 : DEBUG : bound: at document #0\n",
      "2017-11-22 01:10:45,666 : INFO : -8.930 per-word bound, 487.8 perplexity estimate based on a held-out corpus of 300 documents with 18861 words\n",
      "2017-11-22 01:10:45,668 : INFO : PROGRESS: pass 0, at document #300/300\n",
      "2017-11-22 01:10:45,669 : DEBUG : performing inference on a chunk of 300 documents\n",
      "2017-11-22 01:10:46,607 : DEBUG : 1/300 documents converged within 50 iterations\n",
      "2017-11-22 01:10:46,610 : DEBUG : updating topics\n",
      "2017-11-22 01:10:46,728 : INFO : topic #0 (0.500): 0.009*\"australia\" + 0.007*\"government\" + 0.006*\"people\" + 0.005*\"afghanistan\" + 0.005*\"year\" + 0.004*\"company\" + 0.003*\"cent\" + 0.003*\"test\" + 0.003*\"police\" + 0.003*\"day\"\n",
      "2017-11-22 01:10:46,731 : INFO : topic #1 (0.500): 0.006*\"arafat\" + 0.006*\"people\" + 0.005*\"security\" + 0.005*\"government\" + 0.005*\"australia\" + 0.004*\"police\" + 0.004*\"today\" + 0.003*\"israeli\" + 0.003*\"fire\" + 0.003*\"time\"\n",
      "2017-11-22 01:10:46,733 : INFO : topic diff=0.890589, rho=1.000000\n",
      "2017-11-22 01:10:46,736 : DEBUG : Setting topics to those of the model: LdaModel(num_terms=4431, num_topics=2, decay=0.5, chunksize=2000)\n",
      "2017-11-22 01:10:46,738 : INFO : using ParallelWordOccurrenceAccumulator(processes=3, batch_size=64) to estimate probabilities from sliding windows\n",
      "2017-11-22 01:10:46,809 : DEBUG : completed batch 0; 64 documents processed (92 virtual)\n",
      "2017-11-22 01:10:46,827 : DEBUG : completed batch 0; 64 documents processed (271 virtual)\n",
      "2017-11-22 01:10:46,839 : DEBUG : completed batch 0; 64 documents processed (292 virtual)\n",
      "2017-11-22 01:10:46,848 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:10:46,851 : DEBUG : finished all batches; 64 documents processed (292 virtual)\n",
      "2017-11-22 01:10:46,854 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:10:46,858 : INFO : accumulator serialized\n",
      "2017-11-22 01:10:46,877 : DEBUG : completed batch 1; 128 documents processed (354 virtual)\n",
      "2017-11-22 01:10:46,882 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:10:46,884 : DEBUG : finished all batches; 128 documents processed (354 virtual)\n",
      "2017-11-22 01:10:46,887 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:10:46,890 : DEBUG : completed batch 1; 99 documents processed (467 virtual)\n",
      "2017-11-22 01:10:46,894 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:10:46,897 : DEBUG : finished all batches; 99 documents processed (467 virtual)\n",
      "2017-11-22 01:10:46,900 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:10:46,890 : INFO : accumulator serialized\n",
      "2017-11-22 01:10:46,903 : INFO : accumulator serialized\n",
      "2017-11-22 01:10:46,925 : INFO : 3 accumulators retrieved from output queue\n",
      "2017-11-22 01:10:46,940 : INFO : accumulated word occurrence stats for 1113 virtual documents\n",
      "2017-11-22 01:10:47,095 : INFO : using symmetric alpha at 0.3333333333333333\n",
      "2017-11-22 01:10:47,097 : INFO : using symmetric eta at 0.00022568269013766644\n",
      "2017-11-22 01:10:47,100 : INFO : using serial LDA version on this node\n",
      "2017-11-22 01:10:47,268 : INFO : running online (single-pass) LDA training, 3 topics, 1 passes over the supplied corpus of 300 documents, updating model once every 300 documents, evaluating perplexity every 300 documents, iterating 50x with a convergence threshold of 0.001000\n",
      "2017-11-22 01:10:47,271 : WARNING : too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n",
      "2017-11-22 01:10:47,447 : DEBUG : bound: at document #0\n",
      "2017-11-22 01:10:49,308 : INFO : -9.176 per-word bound, 578.4 perplexity estimate based on a held-out corpus of 300 documents with 18861 words\n",
      "2017-11-22 01:10:49,311 : INFO : PROGRESS: pass 0, at document #300/300\n",
      "2017-11-22 01:10:49,313 : DEBUG : performing inference on a chunk of 300 documents\n",
      "2017-11-22 01:10:50,224 : DEBUG : 3/300 documents converged within 50 iterations\n",
      "2017-11-22 01:10:50,226 : DEBUG : updating topics\n",
      "2017-11-22 01:10:50,332 : INFO : topic #0 (0.333): 0.008*\"australia\" + 0.007*\"people\" + 0.006*\"government\" + 0.006*\"security\" + 0.005*\"arafat\" + 0.005*\"police\" + 0.004*\"test\" + 0.004*\"year\" + 0.004*\"time\" + 0.004*\"united_states\"\n",
      "2017-11-22 01:10:50,333 : INFO : topic #1 (0.333): 0.006*\"government\" + 0.004*\"australia\" + 0.004*\"year\" + 0.004*\"people\" + 0.003*\"united_states\" + 0.003*\"today\" + 0.003*\"arafat\" + 0.003*\"day\" + 0.003*\"police\" + 0.003*\"man\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-11-22 01:10:50,335 : INFO : topic #2 (0.333): 0.007*\"australia\" + 0.006*\"people\" + 0.006*\"afghanistan\" + 0.005*\"government\" + 0.004*\"company\" + 0.004*\"days\" + 0.003*\"today\" + 0.003*\"man\" + 0.003*\"metres\" + 0.003*\"fire\"\n",
      "2017-11-22 01:10:50,337 : INFO : topic diff=1.219663, rho=1.000000\n",
      "2017-11-22 01:10:50,340 : DEBUG : Setting topics to those of the model: LdaModel(num_terms=4431, num_topics=3, decay=0.5, chunksize=2000)\n",
      "2017-11-22 01:10:50,343 : INFO : using ParallelWordOccurrenceAccumulator(processes=3, batch_size=64) to estimate probabilities from sliding windows\n",
      "2017-11-22 01:10:50,405 : DEBUG : completed batch 0; 64 documents processed (92 virtual)\n",
      "2017-11-22 01:10:50,437 : DEBUG : completed batch 0; 64 documents processed (305 virtual)\n",
      "2017-11-22 01:10:50,438 : DEBUG : completed batch 0; 64 documents processed (258 virtual)\n",
      "2017-11-22 01:10:50,444 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:10:50,451 : DEBUG : finished all batches; 64 documents processed (258 virtual)\n",
      "2017-11-22 01:10:50,459 : DEBUG : completed batch 1; 128 documents processed (354 virtual)\n",
      "2017-11-22 01:10:50,459 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:10:50,463 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:10:50,465 : INFO : accumulator serialized\n",
      "2017-11-22 01:10:50,478 : DEBUG : finished all batches; 128 documents processed (354 virtual)\n",
      "2017-11-22 01:10:50,482 : DEBUG : completed batch 1; 104 documents processed (506 virtual)\n",
      "2017-11-22 01:10:50,487 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:10:50,491 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:10:50,500 : DEBUG : finished all batches; 104 documents processed (506 virtual)\n",
      "2017-11-22 01:10:50,499 : INFO : accumulator serialized\n",
      "2017-11-22 01:10:50,507 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:10:50,520 : INFO : accumulator serialized\n",
      "2017-11-22 01:10:50,538 : INFO : 3 accumulators retrieved from output queue\n",
      "2017-11-22 01:10:50,558 : INFO : accumulated word occurrence stats for 1118 virtual documents\n",
      "2017-11-22 01:10:50,758 : INFO : using symmetric alpha at 0.25\n",
      "2017-11-22 01:10:50,760 : INFO : using symmetric eta at 0.00022568269013766644\n",
      "2017-11-22 01:10:50,762 : INFO : using serial LDA version on this node\n",
      "2017-11-22 01:10:50,959 : INFO : running online (single-pass) LDA training, 4 topics, 1 passes over the supplied corpus of 300 documents, updating model once every 300 documents, evaluating perplexity every 300 documents, iterating 50x with a convergence threshold of 0.001000\n",
      "2017-11-22 01:10:50,962 : WARNING : too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n",
      "2017-11-22 01:10:51,203 : DEBUG : bound: at document #0\n",
      "2017-11-22 01:10:53,037 : INFO : -9.457 per-word bound, 702.6 perplexity estimate based on a held-out corpus of 300 documents with 18861 words\n",
      "2017-11-22 01:10:53,040 : INFO : PROGRESS: pass 0, at document #300/300\n",
      "2017-11-22 01:10:53,042 : DEBUG : performing inference on a chunk of 300 documents\n",
      "2017-11-22 01:10:53,932 : DEBUG : 7/300 documents converged within 50 iterations\n",
      "2017-11-22 01:10:53,935 : DEBUG : updating topics\n",
      "2017-11-22 01:10:54,047 : INFO : topic #0 (0.250): 0.008*\"australia\" + 0.007*\"company\" + 0.005*\"government\" + 0.005*\"people\" + 0.005*\"afghanistan\" + 0.004*\"cent\" + 0.004*\"time\" + 0.004*\"security\" + 0.003*\"year\" + 0.003*\"forces\"\n",
      "2017-11-22 01:10:54,051 : INFO : topic #1 (0.250): 0.009*\"australia\" + 0.006*\"arafat\" + 0.005*\"security\" + 0.005*\"government\" + 0.005*\"people\" + 0.004*\"today\" + 0.004*\"president\" + 0.004*\"united_states\" + 0.003*\"police\" + 0.003*\"attack\"\n",
      "2017-11-22 01:10:54,052 : INFO : topic #2 (0.250): 0.010*\"people\" + 0.006*\"australia\" + 0.006*\"government\" + 0.005*\"afghanistan\" + 0.005*\"police\" + 0.005*\"taliban\" + 0.005*\"man\" + 0.004*\"year\" + 0.004*\"united_states\" + 0.004*\"today\"\n",
      "2017-11-22 01:10:54,055 : INFO : topic #3 (0.250): 0.007*\"government\" + 0.006*\"fire\" + 0.005*\"arafat\" + 0.004*\"people\" + 0.004*\"police\" + 0.004*\"israel\" + 0.004*\"test\" + 0.004*\"commission\" + 0.003*\"australia\" + 0.003*\"israeli\"\n",
      "2017-11-22 01:10:54,056 : INFO : topic diff=1.652509, rho=1.000000\n",
      "2017-11-22 01:10:54,059 : DEBUG : Setting topics to those of the model: LdaModel(num_terms=4431, num_topics=4, decay=0.5, chunksize=2000)\n",
      "2017-11-22 01:10:54,062 : INFO : using ParallelWordOccurrenceAccumulator(processes=3, batch_size=64) to estimate probabilities from sliding windows\n",
      "2017-11-22 01:10:54,126 : DEBUG : completed batch 0; 64 documents processed (92 virtual)\n",
      "2017-11-22 01:10:54,165 : DEBUG : completed batch 0; 64 documents processed (305 virtual)\n",
      "2017-11-22 01:10:54,166 : DEBUG : completed batch 0; 64 documents processed (258 virtual)\n",
      "2017-11-22 01:10:54,171 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:10:54,174 : DEBUG : finished all batches; 64 documents processed (258 virtual)\n",
      "2017-11-22 01:10:54,180 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:10:54,186 : INFO : accumulator serialized\n",
      "2017-11-22 01:10:54,194 : DEBUG : completed batch 1; 128 documents processed (333 virtual)\n",
      "2017-11-22 01:10:54,201 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:10:54,204 : DEBUG : finished all batches; 128 documents processed (333 virtual)\n",
      "2017-11-22 01:10:54,207 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:10:54,212 : INFO : accumulator serialized\n",
      "2017-11-22 01:10:54,241 : DEBUG : completed batch 1; 106 documents processed (529 virtual)\n",
      "2017-11-22 01:10:54,243 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:10:54,247 : DEBUG : finished all batches; 106 documents processed (529 virtual)\n",
      "2017-11-22 01:10:54,250 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:10:54,258 : INFO : accumulator serialized\n",
      "2017-11-22 01:10:54,277 : INFO : 3 accumulators retrieved from output queue\n",
      "2017-11-22 01:10:54,291 : INFO : accumulated word occurrence stats for 1120 virtual documents\n",
      "2017-11-22 01:10:54,548 : INFO : using symmetric alpha at 0.2\n",
      "2017-11-22 01:10:54,550 : INFO : using symmetric eta at 0.00022568269013766644\n",
      "2017-11-22 01:10:54,553 : INFO : using serial LDA version on this node\n",
      "2017-11-22 01:10:54,789 : INFO : running online (single-pass) LDA training, 5 topics, 1 passes over the supplied corpus of 300 documents, updating model once every 300 documents, evaluating perplexity every 300 documents, iterating 50x with a convergence threshold of 0.001000\n",
      "2017-11-22 01:10:54,791 : WARNING : too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n",
      "2017-11-22 01:10:55,100 : DEBUG : bound: at document #0\n",
      "2017-11-22 01:10:57,200 : INFO : -9.763 per-word bound, 868.6 perplexity estimate based on a held-out corpus of 300 documents with 18861 words\n",
      "2017-11-22 01:10:57,201 : INFO : PROGRESS: pass 0, at document #300/300\n",
      "2017-11-22 01:10:57,203 : DEBUG : performing inference on a chunk of 300 documents\n",
      "2017-11-22 01:10:58,266 : DEBUG : 19/300 documents converged within 50 iterations\n",
      "2017-11-22 01:10:58,269 : DEBUG : updating topics\n",
      "2017-11-22 01:10:58,380 : INFO : topic #0 (0.200): 0.011*\"government\" + 0.006*\"australia\" + 0.004*\"people\" + 0.004*\"arafat\" + 0.004*\"israeli\" + 0.004*\"israel\" + 0.003*\"police\" + 0.003*\"fire\" + 0.003*\"hamas\" + 0.003*\"peace\"\n",
      "2017-11-22 01:10:58,382 : INFO : topic #1 (0.200): 0.010*\"people\" + 0.006*\"australia\" + 0.005*\"company\" + 0.005*\"police\" + 0.005*\"security\" + 0.004*\"commission\" + 0.004*\"government\" + 0.004*\"afghanistan\" + 0.004*\"united_states\" + 0.004*\"year\"\n",
      "2017-11-22 01:10:58,385 : INFO : topic #2 (0.200): 0.006*\"australia\" + 0.005*\"arafat\" + 0.004*\"people\" + 0.004*\"fire\" + 0.004*\"today\" + 0.004*\"afghanistan\" + 0.004*\"years\" + 0.004*\"time\" + 0.003*\"forces\" + 0.003*\"cent\"\n",
      "2017-11-22 01:10:58,387 : INFO : topic #3 (0.200): 0.008*\"afghanistan\" + 0.006*\"united_states\" + 0.005*\"government\" + 0.004*\"afghan\" + 0.004*\"people\" + 0.004*\"australia\" + 0.004*\"year\" + 0.004*\"laden\" + 0.004*\"forces\" + 0.004*\"security\"\n",
      "2017-11-22 01:10:58,389 : INFO : topic #4 (0.200): 0.011*\"australia\" + 0.006*\"people\" + 0.005*\"government\" + 0.005*\"test\" + 0.005*\"arafat\" + 0.004*\"time\" + 0.004*\"police\" + 0.004*\"today\" + 0.004*\"day\" + 0.003*\"power\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-11-22 01:10:58,391 : INFO : topic diff=2.101508, rho=1.000000\n",
      "2017-11-22 01:10:58,394 : DEBUG : Setting topics to those of the model: LdaModel(num_terms=4431, num_topics=5, decay=0.5, chunksize=2000)\n",
      "2017-11-22 01:10:58,396 : INFO : using ParallelWordOccurrenceAccumulator(processes=3, batch_size=64) to estimate probabilities from sliding windows\n",
      "2017-11-22 01:10:58,474 : DEBUG : completed batch 0; 64 documents processed (92 virtual)\n",
      "2017-11-22 01:10:58,506 : DEBUG : completed batch 0; 64 documents processed (258 virtual)\n",
      "2017-11-22 01:10:58,509 : DEBUG : completed batch 0; 64 documents processed (305 virtual)\n",
      "2017-11-22 01:10:58,514 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:10:58,518 : DEBUG : finished all batches; 64 documents processed (305 virtual)\n",
      "2017-11-22 01:10:58,522 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:10:58,528 : INFO : accumulator serialized\n",
      "2017-11-22 01:10:58,533 : DEBUG : completed batch 1; 128 documents processed (332 virtual)\n",
      "2017-11-22 01:10:58,537 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:10:58,541 : DEBUG : finished all batches; 128 documents processed (332 virtual)\n",
      "2017-11-22 01:10:58,545 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:10:58,550 : INFO : accumulator serialized\n",
      "2017-11-22 01:10:58,562 : DEBUG : completed batch 1; 107 documents processed (484 virtual)\n",
      "2017-11-22 01:10:58,564 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:10:58,567 : DEBUG : finished all batches; 107 documents processed (484 virtual)\n",
      "2017-11-22 01:10:58,570 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:10:58,573 : INFO : accumulator serialized\n",
      "2017-11-22 01:10:58,602 : INFO : 3 accumulators retrieved from output queue\n",
      "2017-11-22 01:10:58,625 : INFO : accumulated word occurrence stats for 1121 virtual documents\n",
      "2017-11-22 01:10:58,996 : INFO : using symmetric alpha at 0.16666666666666666\n",
      "2017-11-22 01:10:58,999 : INFO : using symmetric eta at 0.00022568269013766644\n",
      "2017-11-22 01:10:59,002 : INFO : using serial LDA version on this node\n",
      "2017-11-22 01:10:59,293 : INFO : running online (single-pass) LDA training, 6 topics, 1 passes over the supplied corpus of 300 documents, updating model once every 300 documents, evaluating perplexity every 300 documents, iterating 50x with a convergence threshold of 0.001000\n",
      "2017-11-22 01:10:59,296 : WARNING : too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n",
      "2017-11-22 01:10:59,707 : DEBUG : bound: at document #0\n",
      "2017-11-22 01:11:01,872 : INFO : -10.095 per-word bound, 1093.9 perplexity estimate based on a held-out corpus of 300 documents with 18861 words\n",
      "2017-11-22 01:11:01,874 : INFO : PROGRESS: pass 0, at document #300/300\n",
      "2017-11-22 01:11:01,876 : DEBUG : performing inference on a chunk of 300 documents\n",
      "2017-11-22 01:11:02,815 : DEBUG : 22/300 documents converged within 50 iterations\n",
      "2017-11-22 01:11:02,817 : DEBUG : updating topics\n",
      "2017-11-22 01:11:02,931 : INFO : topic #0 (0.167): 0.008*\"arafat\" + 0.007*\"government\" + 0.005*\"year\" + 0.005*\"report\" + 0.005*\"australia\" + 0.004*\"forces\" + 0.004*\"people\" + 0.004*\"union\" + 0.004*\"afghanistan\" + 0.004*\"commission\"\n",
      "2017-11-22 01:11:02,934 : INFO : topic #2 (0.167): 0.008*\"people\" + 0.006*\"government\" + 0.005*\"australia\" + 0.005*\"president\" + 0.005*\"israeli\" + 0.004*\"police\" + 0.004*\"afghanistan\" + 0.004*\"meeting\" + 0.004*\"day\" + 0.004*\"security\"\n",
      "2017-11-22 01:11:02,936 : INFO : topic #3 (0.167): 0.010*\"government\" + 0.006*\"taliban\" + 0.006*\"qantas\" + 0.005*\"company\" + 0.004*\"unions\" + 0.004*\"workers\" + 0.004*\"people\" + 0.004*\"afghanistan\" + 0.004*\"commission\" + 0.004*\"year\"\n",
      "2017-11-22 01:11:02,938 : INFO : topic #4 (0.167): 0.012*\"australia\" + 0.006*\"people\" + 0.006*\"government\" + 0.005*\"today\" + 0.005*\"world\" + 0.004*\"cent\" + 0.004*\"sydney\" + 0.004*\"security\" + 0.003*\"company\" + 0.003*\"year\"\n",
      "2017-11-22 01:11:02,941 : INFO : topic #1 (0.167): 0.010*\"australia\" + 0.006*\"man\" + 0.006*\"afghanistan\" + 0.006*\"people\" + 0.005*\"pakistan\" + 0.005*\"united_states\" + 0.004*\"government\" + 0.004*\"laden\" + 0.004*\"company\" + 0.004*\"test\"\n",
      "2017-11-22 01:11:02,943 : INFO : topic diff=2.745775, rho=1.000000\n",
      "2017-11-22 01:11:02,947 : DEBUG : Setting topics to those of the model: LdaModel(num_terms=4431, num_topics=6, decay=0.5, chunksize=2000)\n",
      "2017-11-22 01:11:02,951 : INFO : using ParallelWordOccurrenceAccumulator(processes=3, batch_size=64) to estimate probabilities from sliding windows\n",
      "2017-11-22 01:11:03,027 : DEBUG : completed batch 0; 64 documents processed (92 virtual)\n",
      "2017-11-22 01:11:03,064 : DEBUG : completed batch 0; 64 documents processed (258 virtual)\n",
      "2017-11-22 01:11:03,081 : DEBUG : completed batch 0; 64 documents processed (305 virtual)\n",
      "2017-11-22 01:11:03,087 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:11:03,090 : DEBUG : finished all batches; 64 documents processed (305 virtual)\n",
      "2017-11-22 01:11:03,093 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:11:03,099 : INFO : accumulator serialized\n",
      "2017-11-22 01:11:03,127 : DEBUG : completed batch 1; 128 documents processed (333 virtual)\n",
      "2017-11-22 01:11:03,130 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:11:03,134 : DEBUG : finished all batches; 128 documents processed (333 virtual)\n",
      "2017-11-22 01:11:03,137 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:11:03,147 : DEBUG : completed batch 1; 106 documents processed (482 virtual)\n",
      "2017-11-22 01:11:03,150 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:11:03,154 : DEBUG : finished all batches; 106 documents processed (482 virtual)\n",
      "2017-11-22 01:11:03,157 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:11:03,140 : INFO : accumulator serialized\n",
      "2017-11-22 01:11:03,162 : INFO : accumulator serialized\n",
      "2017-11-22 01:11:03,185 : INFO : 3 accumulators retrieved from output queue\n",
      "2017-11-22 01:11:03,203 : INFO : accumulated word occurrence stats for 1120 virtual documents\n",
      "2017-11-22 01:11:03,574 : INFO : using symmetric alpha at 0.14285714285714285\n",
      "2017-11-22 01:11:03,576 : INFO : using symmetric eta at 0.00022568269013766644\n",
      "2017-11-22 01:11:03,580 : INFO : using serial LDA version on this node\n",
      "2017-11-22 01:11:03,904 : INFO : running online (single-pass) LDA training, 7 topics, 1 passes over the supplied corpus of 300 documents, updating model once every 300 documents, evaluating perplexity every 300 documents, iterating 50x with a convergence threshold of 0.001000\n",
      "2017-11-22 01:11:03,907 : WARNING : too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n",
      "2017-11-22 01:11:04,370 : DEBUG : bound: at document #0\n",
      "2017-11-22 01:11:06,336 : INFO : -10.452 per-word bound, 1400.8 perplexity estimate based on a held-out corpus of 300 documents with 18861 words\n",
      "2017-11-22 01:11:06,339 : INFO : PROGRESS: pass 0, at document #300/300\n",
      "2017-11-22 01:11:06,341 : DEBUG : performing inference on a chunk of 300 documents\n",
      "2017-11-22 01:11:07,289 : DEBUG : 22/300 documents converged within 50 iterations\n",
      "2017-11-22 01:11:07,291 : DEBUG : updating topics\n",
      "2017-11-22 01:11:07,409 : INFO : topic #0 (0.143): 0.007*\"fire\" + 0.007*\"metres\" + 0.006*\"australia\" + 0.005*\"sydney\" + 0.004*\"action\" + 0.004*\"night\" + 0.004*\"yesterday\" + 0.003*\"company\" + 0.003*\"event\" + 0.003*\"police\"\n",
      "2017-11-22 01:11:07,412 : INFO : topic #3 (0.143): 0.013*\"australia\" + 0.011*\"government\" + 0.009*\"afghanistan\" + 0.008*\"people\" + 0.007*\"test\" + 0.005*\"united_states\" + 0.004*\"day\" + 0.004*\"match\" + 0.004*\"team\" + 0.004*\"force\"\n",
      "2017-11-22 01:11:07,415 : INFO : topic #4 (0.143): 0.005*\"year\" + 0.005*\"sydney\" + 0.004*\"time\" + 0.004*\"government\" + 0.004*\"people\" + 0.004*\"today\" + 0.004*\"security\" + 0.004*\"police\" + 0.004*\"afghanistan\" + 0.004*\"fire\"\n",
      "2017-11-22 01:11:07,418 : INFO : topic #2 (0.143): 0.008*\"man\" + 0.007*\"australia\" + 0.006*\"security\" + 0.006*\"fire\" + 0.005*\"pakistan\" + 0.004*\"people\" + 0.004*\"year\" + 0.004*\"officials\" + 0.004*\"south\" + 0.003*\"today\"\n",
      "2017-11-22 01:11:07,420 : INFO : topic #1 (0.143): 0.007*\"commission\" + 0.006*\"government\" + 0.005*\"people\" + 0.005*\"arafat\" + 0.005*\"union\" + 0.005*\"today\" + 0.004*\"world\" + 0.004*\"year\" + 0.004*\"peace\" + 0.003*\"day\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-11-22 01:11:07,423 : INFO : topic diff=3.376669, rho=1.000000\n",
      "2017-11-22 01:11:07,426 : DEBUG : Setting topics to those of the model: LdaModel(num_terms=4431, num_topics=7, decay=0.5, chunksize=2000)\n",
      "2017-11-22 01:11:07,429 : INFO : using ParallelWordOccurrenceAccumulator(processes=3, batch_size=64) to estimate probabilities from sliding windows\n",
      "2017-11-22 01:11:07,512 : DEBUG : completed batch 0; 64 documents processed (92 virtual)\n",
      "2017-11-22 01:11:07,564 : DEBUG : completed batch 0; 64 documents processed (258 virtual)\n",
      "2017-11-22 01:11:07,568 : DEBUG : completed batch 0; 64 documents processed (305 virtual)\n",
      "2017-11-22 01:11:07,575 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:11:07,582 : DEBUG : finished all batches; 64 documents processed (305 virtual)\n",
      "2017-11-22 01:11:07,587 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:11:07,596 : INFO : accumulator serialized\n",
      "2017-11-22 01:11:07,594 : DEBUG : completed batch 1; 128 documents processed (332 virtual)\n",
      "2017-11-22 01:11:07,600 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:11:07,613 : DEBUG : finished all batches; 128 documents processed (332 virtual)\n",
      "2017-11-22 01:11:07,619 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:11:07,632 : INFO : accumulator serialized\n",
      "2017-11-22 01:11:07,647 : DEBUG : completed batch 1; 108 documents processed (485 virtual)\n",
      "2017-11-22 01:11:07,650 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:11:07,653 : DEBUG : finished all batches; 108 documents processed (485 virtual)\n",
      "2017-11-22 01:11:07,657 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:11:07,666 : INFO : accumulator serialized\n",
      "2017-11-22 01:11:07,689 : INFO : 3 accumulators retrieved from output queue\n",
      "2017-11-22 01:11:07,708 : INFO : accumulated word occurrence stats for 1122 virtual documents\n",
      "2017-11-22 01:11:08,185 : INFO : using symmetric alpha at 0.125\n",
      "2017-11-22 01:11:08,187 : INFO : using symmetric eta at 0.00022568269013766644\n",
      "2017-11-22 01:11:08,191 : INFO : using serial LDA version on this node\n",
      "2017-11-22 01:11:08,592 : INFO : running online (single-pass) LDA training, 8 topics, 1 passes over the supplied corpus of 300 documents, updating model once every 300 documents, evaluating perplexity every 300 documents, iterating 50x with a convergence threshold of 0.001000\n",
      "2017-11-22 01:11:08,594 : WARNING : too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n",
      "2017-11-22 01:11:09,099 : DEBUG : bound: at document #0\n",
      "2017-11-22 01:11:10,965 : INFO : -10.837 per-word bound, 1829.8 perplexity estimate based on a held-out corpus of 300 documents with 18861 words\n",
      "2017-11-22 01:11:10,966 : INFO : PROGRESS: pass 0, at document #300/300\n",
      "2017-11-22 01:11:10,968 : DEBUG : performing inference on a chunk of 300 documents\n",
      "2017-11-22 01:11:11,912 : DEBUG : 29/300 documents converged within 50 iterations\n",
      "2017-11-22 01:11:11,915 : DEBUG : updating topics\n",
      "2017-11-22 01:11:12,035 : INFO : topic #0 (0.125): 0.005*\"staff\" + 0.004*\"new_zealand\" + 0.004*\"security\" + 0.004*\"man\" + 0.004*\"australia\" + 0.003*\"people\" + 0.003*\"aircraft\" + 0.003*\"year\" + 0.003*\"information\" + 0.003*\"force\"\n",
      "2017-11-22 01:11:12,037 : INFO : topic #1 (0.125): 0.012*\"people\" + 0.008*\"australia\" + 0.006*\"year\" + 0.006*\"test\" + 0.005*\"government\" + 0.005*\"metres\" + 0.005*\"cent\" + 0.004*\"report\" + 0.004*\"police\" + 0.004*\"children\"\n",
      "2017-11-22 01:11:12,039 : INFO : topic #3 (0.125): 0.006*\"israel\" + 0.006*\"arafat\" + 0.005*\"fire\" + 0.005*\"israeli\" + 0.004*\"hospital\" + 0.004*\"people\" + 0.004*\"officials\" + 0.004*\"station\" + 0.003*\"space\" + 0.003*\"west_bank\"\n",
      "2017-11-22 01:11:12,041 : INFO : topic #6 (0.125): 0.007*\"australia\" + 0.007*\"commission\" + 0.006*\"year\" + 0.006*\"world\" + 0.006*\"today\" + 0.005*\"company\" + 0.005*\"united_states\" + 0.004*\"economy\" + 0.004*\"taliban\" + 0.004*\"cent\"\n",
      "2017-11-22 01:11:12,044 : INFO : topic #7 (0.125): 0.012*\"government\" + 0.007*\"people\" + 0.006*\"afghanistan\" + 0.005*\"forces\" + 0.005*\"pakistan\" + 0.005*\"taliban\" + 0.005*\"president\" + 0.004*\"australia\" + 0.004*\"state\" + 0.004*\"security\"\n",
      "2017-11-22 01:11:12,046 : INFO : topic diff=4.066159, rho=1.000000\n",
      "2017-11-22 01:11:12,050 : DEBUG : Setting topics to those of the model: LdaModel(num_terms=4431, num_topics=8, decay=0.5, chunksize=2000)\n",
      "2017-11-22 01:11:12,053 : INFO : using ParallelWordOccurrenceAccumulator(processes=3, batch_size=64) to estimate probabilities from sliding windows\n",
      "2017-11-22 01:11:12,146 : DEBUG : completed batch 0; 64 documents processed (92 virtual)\n",
      "2017-11-22 01:11:12,195 : DEBUG : completed batch 0; 64 documents processed (305 virtual)\n",
      "2017-11-22 01:11:12,196 : DEBUG : completed batch 0; 64 documents processed (258 virtual)\n",
      "2017-11-22 01:11:12,203 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:11:12,209 : DEBUG : finished all batches; 64 documents processed (258 virtual)\n",
      "2017-11-22 01:11:12,216 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:11:12,242 : DEBUG : completed batch 1; 128 documents processed (332 virtual)\n",
      "2017-11-22 01:11:12,225 : INFO : accumulator serialized\n",
      "2017-11-22 01:11:12,248 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:11:12,258 : DEBUG : finished all batches; 128 documents processed (332 virtual)\n",
      "2017-11-22 01:11:12,270 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:11:12,274 : INFO : accumulator serialized\n",
      "2017-11-22 01:11:12,283 : DEBUG : completed batch 1; 107 documents processed (531 virtual)\n",
      "2017-11-22 01:11:12,288 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:11:12,291 : DEBUG : finished all batches; 107 documents processed (531 virtual)\n",
      "2017-11-22 01:11:12,295 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:11:12,301 : INFO : accumulator serialized\n",
      "2017-11-22 01:11:12,330 : INFO : 3 accumulators retrieved from output queue\n",
      "2017-11-22 01:11:12,352 : INFO : accumulated word occurrence stats for 1121 virtual documents\n",
      "2017-11-22 01:11:12,879 : INFO : using symmetric alpha at 0.1111111111111111\n",
      "2017-11-22 01:11:12,880 : INFO : using symmetric eta at 0.00022568269013766644\n",
      "2017-11-22 01:11:12,883 : INFO : using serial LDA version on this node\n",
      "2017-11-22 01:11:13,312 : INFO : running online (single-pass) LDA training, 9 topics, 1 passes over the supplied corpus of 300 documents, updating model once every 300 documents, evaluating perplexity every 300 documents, iterating 50x with a convergence threshold of 0.001000\n",
      "2017-11-22 01:11:13,315 : WARNING : too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n",
      "2017-11-22 01:11:13,923 : DEBUG : bound: at document #0\n",
      "2017-11-22 01:11:15,803 : INFO : -11.246 per-word bound, 2429.4 perplexity estimate based on a held-out corpus of 300 documents with 18861 words\n",
      "2017-11-22 01:11:15,806 : INFO : PROGRESS: pass 0, at document #300/300\n",
      "2017-11-22 01:11:15,808 : DEBUG : performing inference on a chunk of 300 documents\n",
      "2017-11-22 01:11:16,766 : DEBUG : 23/300 documents converged within 50 iterations\n",
      "2017-11-22 01:11:16,768 : DEBUG : updating topics\n",
      "2017-11-22 01:11:16,889 : INFO : topic #6 (0.111): 0.007*\"government\" + 0.006*\"australia\" + 0.005*\"afghanistan\" + 0.005*\"people\" + 0.005*\"taliban\" + 0.004*\"time\" + 0.004*\"today\" + 0.004*\"man\" + 0.004*\"sydney\" + 0.004*\"force\"\n",
      "2017-11-22 01:11:16,892 : INFO : topic #0 (0.111): 0.008*\"australia\" + 0.006*\"government\" + 0.006*\"test\" + 0.006*\"team\" + 0.006*\"hamas\" + 0.006*\"arafat\" + 0.005*\"israel\" + 0.004*\"sydney\" + 0.004*\"attack\" + 0.004*\"police\"\n",
      "2017-11-22 01:11:16,894 : INFO : topic #5 (0.111): 0.009*\"afghanistan\" + 0.009*\"government\" + 0.008*\"afghan\" + 0.007*\"australia\" + 0.006*\"people\" + 0.006*\"forces\" + 0.006*\"security\" + 0.005*\"days\" + 0.005*\"president\" + 0.005*\"pakistan\"\n",
      "2017-11-22 01:11:16,896 : INFO : topic #7 (0.111): 0.007*\"australia\" + 0.006*\"government\" + 0.005*\"israeli\" + 0.005*\"arafat\" + 0.004*\"india\" + 0.004*\"years\" + 0.004*\"east_timor\" + 0.004*\"year\" + 0.004*\"peace\" + 0.004*\"today\"\n",
      "2017-11-22 01:11:16,899 : INFO : topic #4 (0.111): 0.013*\"australia\" + 0.008*\"people\" + 0.007*\"man\" + 0.007*\"government\" + 0.007*\"company\" + 0.005*\"action\" + 0.004*\"year\" + 0.004*\"qantas\" + 0.004*\"police\" + 0.004*\"unions\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-11-22 01:11:16,901 : INFO : topic diff=4.736394, rho=1.000000\n",
      "2017-11-22 01:11:16,904 : DEBUG : Setting topics to those of the model: LdaModel(num_terms=4431, num_topics=9, decay=0.5, chunksize=2000)\n",
      "2017-11-22 01:11:16,907 : INFO : using ParallelWordOccurrenceAccumulator(processes=3, batch_size=64) to estimate probabilities from sliding windows\n",
      "2017-11-22 01:11:17,006 : DEBUG : completed batch 0; 64 documents processed (92 virtual)\n",
      "2017-11-22 01:11:17,040 : DEBUG : completed batch 0; 64 documents processed (258 virtual)\n",
      "2017-11-22 01:11:17,058 : DEBUG : completed batch 0; 64 documents processed (305 virtual)\n",
      "2017-11-22 01:11:17,064 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:11:17,067 : DEBUG : finished all batches; 64 documents processed (305 virtual)\n",
      "2017-11-22 01:11:17,072 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:11:17,083 : INFO : accumulator serialized\n",
      "2017-11-22 01:11:17,122 : DEBUG : completed batch 1; 128 documents processed (332 virtual)\n",
      "2017-11-22 01:11:17,124 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:11:17,129 : DEBUG : finished all batches; 128 documents processed (332 virtual)\n",
      "2017-11-22 01:11:17,132 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:11:17,152 : DEBUG : completed batch 1; 108 documents processed (485 virtual)\n",
      "2017-11-22 01:11:17,136 : INFO : accumulator serialized\n",
      "2017-11-22 01:11:17,154 : DEBUG : observed sentinel value; terminating\n",
      "2017-11-22 01:11:17,159 : DEBUG : finished all batches; 108 documents processed (485 virtual)\n",
      "2017-11-22 01:11:17,167 : INFO : serializing accumulator to return to master...\n",
      "2017-11-22 01:11:17,170 : INFO : accumulator serialized\n",
      "2017-11-22 01:11:17,197 : INFO : 3 accumulators retrieved from output queue\n",
      "2017-11-22 01:11:17,220 : INFO : accumulated word occurrence stats for 1122 virtual documents\n"
     ]
    },
    {
     "data": {
      "image/png": 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RqZ+KRQJ6eMpy2mSmcs6IvKCjiEgToWKRYJZuKOKdhRu4aGRPWqZHM1GiiIiK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\niNRLxUJEROqlYiEiIvX6/xQTixZ3HHPkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2494f4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 34 s, sys: 874 ms, total: 34.8 s\n",
      "Wall time: 36 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "lmlist, c_v = evaluate_graph(dictionary=dictionary, corpus=corpus, texts=train_texts, limit=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "nbpresent": {
     "id": "c02b6f0a-801c-4a8f-aa8f-8c9d64df7a9a"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-11-22 01:11:17,989 : DEBUG : performing inference on a chunk of 300 documents\n",
      "2017-11-22 01:11:18,491 : DEBUG : 260/300 documents converged within 50 iterations\n",
      "/Users/home/Desenvolvimento/anaconda3/lib/python3.6/site-packages/pyLDAvis/_prepare.py:387: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  topic_term_dists = topic_term_dists.ix[topic_order]\n"
     ]
    },
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\"gambier\", \"gambier\", \"gambier\", \"gang\", \"gang\", \"gang\", \"ganges\", \"gas\", \"gas\", \"gas\", \"geneva\", \"geoff\", \"geoff\", \"geoff\", \"geoff\", \"geoff\", \"geoff\", \"germany\", \"germany\", \"germany\", \"germany\", \"gillies\", \"gillies\", \"girls\", \"giuliani\", \"giuliani\", \"gold\", \"goshen\", \"goshen\", \"government\", \"government\", \"government\", \"government\", \"government\", \"government\", \"government\", \"government\", \"government\", \"grocery\", \"group\", \"group\", \"group\", \"group\", \"group\", \"group\", \"group\", \"group\", \"group\", \"groups\", \"groups\", \"groups\", \"groups\", \"groups\", \"groups\", \"groups\", \"groups\", \"groups\", \"grundig\", \"guards\", \"guerrilla\", \"gunmen\", \"gunmen\", \"gunmen\", \"gunmen\", \"gutnick\", \"gutnick\", \"ham\", \"hamas\", \"hamas\", \"hamas\", \"hamas\", \"hamas\", \"hamas\", \"hamas\", \"hamas\", \"hamas\", \"hare\", \"harrison\", \"harrison\", \"harrison\", \"harrison\", \"harrison\", \"harrison\", \"hass\", \"hawthorne\", \"hawthorne\", \"head\", \"head\", \"head\", \"head\", \"head\", \"head\", \"head\", \"heater\", \"hence\", \"hewitt\", \"hewitt\", \"hewitt\", \"hewitt\", \"hicks\", \"hicks\", \"hicks\", \"hicks\", \"hicks\", \"high\", \"highway\", \"highway\", \"highway\", \"highway\", \"highway\", \"hih\", \"hih\", \"hih\", \"hih\", \"hih\", \"hindu\", \"hindus\", \"holy\", \"hooke\", \"hopman\", \"hopman\", \"hours\", \"hours\", \"hours\", \"hours\", \"hours\", \"hours\", \"hours\", \"hours\", \"hours\", \"howard\", \"howard\", \"howard\", \"howard\", \"howard\", \"howard\", \"howard\", \"huegill\", \"hume\", \"hume\", \"idea\", \"imf\", \"immersion\", \"immigrants\", \"index\", \"india\", \"india\", \"india\", \"india\", \"india\", \"india\", \"india\", \"india\", \"indiana\", \"indicators\", \"innings\", \"innings\", \"innings\", \"innings\", \"innings\", \"innings\", \"intelligence\", \"intelligence\", \"intelligence\", \"intelligence\", \"interest_rates\", \"interest_rates\", \"interest_rates\", \"interest_rates\", \"interest_rates\", \"iraq\", \"israel\", \"israel\", \"israel\", \"israel\", \"israel\", \"israel\", \"israel\", \"israel\", \"israel\", \"israeli\", \"israeli\", \"israeli\", \"israeli\", \"israeli\", \"israeli\", \"israeli\", \"israeli\", \"israeli\", \"jackson\", \"jefferson\", \"jones\", \"julia\", \"jumping\", \"justification\", \"kabul\", \"kabul\", \"kabul\", \"kabul\", \"kabul\", \"kabul\", \"kabul\", \"kandahar\", \"kandahar\", \"kandahar\", \"kandahar\", \"kandahar\", \"kandahar\", \"kandahar\", \"karzai\", \"karzai\", \"karzai\", \"karzai\", \"karzai\", \"kashmir\", \"kashmir\", \"kashmir\", \"kashmir\", \"kentucky\", \"kieren\", \"kieren\", \"killer\", \"killings\", \"knighthoods\", \"krakatoa\", \"krishna\", \"laden\", \"laden\", \"laden\", \"laden\", \"laden\", \"laden\", \"laden\", \"laden\", \"laden\", \"launceston\", \"launceston\", \"lavarch\", \"lawrence\", \"laws\", \"laws\", \"laws\", \"laws\", \"laws\", \"laws\", \"laws\", \"leader\", \"leader\", \"leader\", \"leader\", \"leader\", \"leader\", \"leader\", \"leader\", \"leaders\", \"leaders\", \"leaders\", \"leaders\", \"leaders\", \"leaders\", \"leaders\", \"leaders\", \"lee\", \"lee\", \"lee\", \"lee\", \"lee\", \"lee\", \"lee\", \"lew\", \"liesel\", \"line\", \"line\", \"line\", \"line\", \"line\", \"line\", \"line\", \"line\", \"lines\", \"lines\", \"lines\", \"literacy\", \"locals\", \"lockett\", \"lockett\", \"lockett\", \"lockett\", \"looks\", \"lording\", \"lording\", \"lording\", \"lubbers\", \"lung\", \"lung\", \"macfarlane\", \"maker\", \"man\", \"man\", \"man\", \"man\", \"man\", \"man\", \"man\", \"man\", \"man\", \"markets\", \"markets\", \"markets\", \"markets\", \"marks\", \"master\", \"masters\", \"match\", \"match\", \"match\", \"match\", \"match\", \"match\", \"match\", \"match\", \"match\", \"matt\", \"mayor\", \"mayor\", \"mayor\", \"mayor\", \"mcdonald\", \"mcgrath\", \"mcgrath\", \"mcgrath\", \"mcmenamin\", \"mcmenamin\", \"meat\", \"meeting\", \"meeting\", \"meeting\", \"meeting\", \"meeting\", \"meeting\", \"meeting\", \"meeting\", \"meeting\", \"melbourne\", \"melbourne\", \"melbourne\", \"melbourne\", \"melbourne\", \"melbourne\", \"melbourne\", \"melbourne\", \"members\", \"members\", \"members\", \"members\", \"members\", \"members\", \"members\", \"members\", \"members\", \"mental\", \"mentha\", \"merit\", \"metres\", \"metres\", \"metres\", \"metres\", \"metres\", \"metres\", \"midwives\", \"midwives\", \"minister\", \"minister\", \"minister\", \"minister\", \"minister\", \"minister\", \"minister\", \"minister\", \"minister\", \"ministerial\", \"mistakes\", \"mistakes\", \"mistrust\", \"mitsubishi\", \"monetary\", \"months\", \"months\", \"months\", \"months\", \"months\", \"months\", \"months\", \"months\", \"morning\", \"morning\", \"morning\", \"morning\", \"morning\", \"morning\", \"morning\", \"morning\", \"morning\", \"morrow\", \"moses\", \"motorists\", \"motorway\", \"moulin\", \"mugabe\", \"needs\", \"needs\", \"needs\", \"nelson\", \"nelson\", \"new_south\", \"new_south\", \"new_south\", \"new_south\", \"new_south\", \"new_south\", \"new_south\", \"new_south\", \"new_york\", \"new_york\", \"new_york\", \"new_york\", \"new_york\", \"new_york\", \"new_york\", \"new_york\", \"new_zealand\", \"new_zealand\", \"new_zealand\", \"new_zealand\", \"new_zealand\", \"newspapers\", \"nicky\", \"night\", \"night\", \"night\", \"night\", \"night\", \"night\", \"night\", \"night\", \"night\", \"notices\", \"oecd\", \"official\", \"official\", \"official\", \"official\", \"official\", \"official\", \"officials\", \"officials\", \"officials\", \"officials\", \"officials\", \"officials\", \"officials\", \"officials\", \"officials\", \"oil\", \"oil\", \"oil\", \"olivier\", \"olympic\", \"olympics\", \"onlookers\", \"organs\", \"outbreak\", \"outbreaks\", \"owen\", \"owen\", \"pacific\", \"pacific\", \"pacific\", \"pakistan\", \"pakistan\", \"pakistan\", \"pakistan\", \"pakistan\", \"pakistan\", \"pakistan\", \"pakistan\", \"palestinians\", \"palestinians\", \"palestinians\", \"palestinians\", \"palestinians\", \"palestinians\", \"palestinians\", \"palestinians\", \"palmerston\", \"palmerston\", \"party\", \"party\", \"party\", \"party\", \"party\", \"party\", \"party\", \"party\", \"passes\", \"pay\", \"pay\", \"pay\", \"pay\", \"payout\", \"peace\", \"peace\", \"peace\", \"peace\", \"peace\", \"peace\", \"peace\", \"peace\", \"peace\", \"people\", \"people\", \"people\", \"people\", \"people\", \"people\", \"people\", \"people\", \"people\", \"peru\", \"petithory\", \"petrol\", \"petrol\", \"petrol\", \"petrol\", \"phenomena\", \"phillips\", \"phillips\", \"phillips\", \"pickup\", \"picton\", \"pieces\", \"place\", \"place\", \"place\", \"place\", \"place\", \"place\", \"place\", \"place\", \"place\", \"police\", \"police\", \"police\", \"police\", \"police\", \"police\", \"police\", \"police\", \"police\", \"pool\", \"population\", \"population\", \"population\", \"population\", \"population\", \"post\", \"post\", \"post\", \"post\", \"post\", \"potts\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"prayer\", \"prayers\", \"president\", \"president\", \"president\", \"president\", \"president\", \"president\", \"president\", \"president\", \"prime_minister\", \"prime_minister\", \"prime_minister\", \"prime_minister\", \"prime_minister\", \"prime_minister\", \"prime_minister\", \"prime_minister\", \"prime_minister\", \"prison\", \"prison\", \"prize\", \"products\", \"professor\", \"professor\", \"professor\", \"professor\", \"project\", \"project\", \"projects\", \"projects\", \"projects\", \"prosser\", \"prostitutes\", \"provisions\", \"qaeda\", \"qaeda\", \"qaeda\", \"qaeda\", \"qaeda\", \"qaeda\", \"qaeda\", \"qaeda\", \"qaeda\", \"qantas\", \"qantas\", \"qantas\", \"qantas\", \"qantas\", \"qantas\", \"qantas\", \"qantas\", \"questions\", \"questions\", \"questions\", \"questions\", \"rabbani\", \"rabbani\", \"race\", \"race\", \"race\", \"race\", \"race\", \"race\", \"races\", \"radio\", \"radio\", \"radio\", \"radio\", \"radio\", \"radio\", \"radio\", \"radio\", \"raduyev\", \"raduyev\", \"rate\", \"rate\", \"rate\", \"rate\", \"rate\", \"rate\", \"rates\", \"rates\", \"ray\", \"ray\", \"ray\", \"ray\", \"razor\", \"razor\", \"rebecca\", \"recruitment\", \"redmond\", \"redmond\", \"refugee\", \"refugees\", \"refugees\", \"reid\", \"reid\", \"reid\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"research\", \"research\", \"research\", \"research\", \"research\", \"reserve_bank\", \"reserve_bank\", \"reserve_bank\", \"return\", \"return\", \"return\", \"return\", \"revenue\", \"rings\", \"rites\", \"river\", \"river\", \"river\", \"river\", \"river\", \"river\", \"river\", \"roads\", \"roads\", \"rome\", \"round\", \"round\", \"route\", \"route\", \"royal\", \"royal\", \"royal\", \"royal\", \"royal\", \"royal\", \"royal\", \"rudd\", \"ruddock\", \"ruddock\", \"ruud\", \"ryan\", \"saadi\", \"saadi\", \"salary\", \"salman\", \"saul\", \"scarfe\", \"scarfe\", \"seconds\", \"sect\", \"sector\", \"sector\", \"sector\", \"sector\", \"sector\", \"sector\", \"security\", \"security\", \"security\", \"security\", \"security\", \"security\", \"security\", \"security\", \"security\", \"segment\", \"sergeant\", \"sergeant\", \"ses\", \"ses\", \"ses\", \"sex\", \"sex\", \"sex\", \"sex\", \"sex\", \"sharing\", \"sharon\", \"sharon\", \"sharon\", \"sharon\", \"sharon\", \"sharon\", \"sharon\", \"sheikh\", \"sheikh\", \"sheikh\", \"shelves\", \"shuttle\", \"shuttle\", \"shuttle\", \"sinai\", \"skins\", \"skipper\", \"skipper\", \"slips\", \"slips\", \"slump\", \"solomon\", \"solomon\", \"solomon\", \"south\", \"south\", \"south\", \"south\", \"south\", \"south\", \"south\", \"south\", \"south_africa\", \"south_africa\", \"south_africa\", \"south_africa\", 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      "text/plain": [
       "PreparedData(topic_coordinates=            Freq  cluster  topics         x         y\n",
       "topic                                                \n",
       "4      14.945762        1       1  0.072089 -0.000847\n",
       "6      13.780041        1       2  0.024113  0.013608\n",
       "8      13.349641        1       3  0.025229  0.039164\n",
       "1      12.806610        1       4  0.004053  0.012297\n",
       "5      12.138166        1       5  0.046273  0.030303\n",
       "0      11.446021        1       6 -0.029974 -0.054651\n",
       "2       8.693269        1       7 -0.008390 -0.073748\n",
       "7       6.731656        1       8 -0.030598 -0.020529\n",
       "3       6.108833        1       9 -0.102793  0.054402, topic_info=     Category        Freq           Term       Total  loglift  logprob\n",
       "term                                                                  \n",
       "912   Default   25.000000         metres   25.000000  30.0000  30.0000\n",
       "295   Default   42.000000          world   42.000000  29.0000  29.0000\n",
       "255   Default   12.000000          event   12.000000  28.0000  28.0000\n",
       "511   Default   38.000000         afghan   38.000000  27.0000  27.0000\n",
       "237   Default   29.000000           team   29.000000  26.0000  26.0000\n",
       "596   Default   41.000000           test   41.000000  25.0000  25.0000\n",
       "35    Default   48.000000           fire   48.000000  24.0000  24.0000\n",
       "628   Default   13.000000    new_zealand   13.000000  23.0000  23.0000\n",
       "845   Default   68.000000         arafat   68.000000  22.0000  22.0000\n",
       "95    Default   28.000000          india   28.000000  21.0000  21.0000\n",
       "537   Default   40.000000        israeli   40.000000  20.0000  20.0000\n",
       "512   Default   44.000000      officials   44.000000  19.0000  19.0000\n",
       "625   Default   41.000000           cent   41.000000  18.0000  18.0000\n",
       "220   Default   75.000000    afghanistan   75.000000  17.0000  17.0000\n",
       "1659  Default   10.000000     east_timor   10.000000  16.0000  16.0000\n",
       "1665  Default   53.000000        company   53.000000  15.0000  15.0000\n",
       "42    Default   64.000000           year   64.000000  14.0000  14.0000\n",
       "1562  Default   29.000000          hamas   29.000000  13.0000  13.0000\n",
       "2584  Default   14.000000          space   14.000000  12.0000  12.0000\n",
       "772   Default    8.000000          blake    8.000000  11.0000  11.0000\n",
       "165   Default  111.000000     government  111.000000  10.0000  10.0000\n",
       "3842  Default    4.000000          races    4.000000   9.0000   9.0000\n",
       "2489  Default   19.000000         sharon   19.000000   8.0000   8.0000\n",
       "2380  Default   25.000000         unions   25.000000   7.0000   7.0000\n",
       "909   Default   15.000000          water   15.000000   6.0000   6.0000\n",
       "1679  Default   15.000000            pay   15.000000   5.0000   5.0000\n",
       "131   Default  129.000000      australia  129.000000   4.0000   4.0000\n",
       "1150  Default   21.000000          radio   21.000000   3.0000   3.0000\n",
       "112   Default   35.000000       pakistan   35.000000   2.0000   2.0000\n",
       "758   Default   12.000000           race   12.000000   1.0000   1.0000\n",
       "...       ...         ...            ...         ...      ...      ...\n",
       "295    Topic9    9.606025          world   42.628706   1.3053  -4.7870\n",
       "909    Topic9    4.730574          water   15.690060   1.5965  -5.4954\n",
       "787    Topic9    2.174189          round    4.338060   2.1047  -6.2728\n",
       "182    Topic9    3.761713          women   11.745709   1.6568  -5.7245\n",
       "628    Topic9    3.770057    new_zealand   13.824314   1.4961  -5.7223\n",
       "758    Topic9    3.521037           race   12.260787   1.5478  -5.7907\n",
       "845    Topic9    8.156799         arafat   68.353974   0.6696  -4.9506\n",
       "512    Topic9    6.394931      officials   44.364007   0.8585  -5.1939\n",
       "320    Topic9    3.323947           line   13.977836   1.3591  -5.8483\n",
       "3478   Topic9    2.100791       champion    5.099200   1.9087  -6.3071\n",
       "246    Topic9    3.185176            cup   13.050772   1.3851  -5.8909\n",
       "2489   Topic9    3.588797         sharon   19.359648   1.1101  -5.7716\n",
       "3435   Topic9    1.626856          prize    2.950670   2.2001  -6.5628\n",
       "236    Topic9    5.569404  united_states   58.222938   0.4484  -5.3321\n",
       "333    Topic9    4.142364          night   30.543189   0.7976  -5.6282\n",
       "159    Topic9    3.839326         leader   26.719061   0.8554  -5.7041\n",
       "313    Topic9    3.635280      melbourne   27.479481   0.7727  -5.7587\n",
       "90     Topic9    5.000222         police   69.308374   0.1664  -5.4399\n",
       "583    Topic9    2.252872          geoff    7.907364   1.5398  -6.2372\n",
       "35     Topic9    4.075622           fire   48.970853   0.3092  -5.6444\n",
       "131    Topic9    4.587872      australia  129.838461  -0.5474  -5.5260\n",
       "1      Topic9    3.988122         people  116.086303  -0.5756  -5.6661\n",
       "220    Topic9    3.682420    afghanistan   75.392864  -0.2237  -5.7458\n",
       "125    Topic9    3.441811           time   55.462812   0.0157  -5.8134\n",
       "465    Topic9    3.061426          union   27.794006   0.5895  -5.9305\n",
       "679    Topic9    3.126033            man   53.079705  -0.0366  -5.9097\n",
       "1324   Topic9    3.089411        attacks   41.355333   0.2012  -5.9214\n",
       "237    Topic9    2.972091           team   29.215842   0.5100  -5.9602\n",
       "452    Topic9    2.910673          peace   25.639618   0.6197  -5.9810\n",
       "165    Topic9    2.981935     government  111.490329  -0.8259  -5.9568\n",
       "\n",
       "[678 rows x 6 columns], token_table=      Topic      Freq         Term\n",
       "term                              \n",
       "1637      1  0.073824        abuse\n",
       "1637      2  0.442944        abuse\n",
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       "3820      7  0.674183  advertising\n",
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       "511       4  0.051762       afghan\n",
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       "511       6  0.077643       afghan\n",
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       "220       3  0.172430  afghanistan\n",
       "220       4  0.145902  afghanistan\n",
       "220       5  0.278541  afghanistan\n",
       "220       6  0.066319  afghanistan\n",
       "...     ...       ...          ...\n",
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       "152       7  0.126297        years\n",
       "152       8  0.126297        years\n",
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       "111       1  0.096051    yesterday\n",
       "111       2  0.120063    yesterday\n",
       "111       3  0.144076    yesterday\n",
       "111       4  0.216114    yesterday\n",
       "111       5  0.120063    yesterday\n",
       "111       6  0.120063    yesterday\n",
       "111       7  0.096051    yesterday\n",
       "111       8  0.048025    yesterday\n",
       "111       9  0.048025    yesterday\n",
       "575       4  0.789004         zeus\n",
       "1911      1  0.811049     zimbabwe\n",
       "1911      2  0.115864     zimbabwe\n",
       "1911      8  0.115864     zimbabwe\n",
       "835       1  0.108530        zinni\n",
       "835       2  0.108530        zinni\n",
       "835       4  0.108530        zinni\n",
       "835       6  0.217059        zinni\n",
       "835       8  0.325589        zinni\n",
       "\n",
       "[1702 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[5, 7, 9, 2, 6, 1, 3, 8, 4])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.gensim.prepare(lmlist[8], corpus, dictionary)"
   ]
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  {
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   "metadata": {},
   "source": [
    "# How this topic model can be used further\n",
    "The best topic model here can be used as a standalone for news article classification. However a topic model can also be used as a dimensionality reduction algorithm to feed into a classifier. A good topic model should be able to extract the signal from the noise efficiently, hence improving the performance of the classifier."
   ]
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