Tutorial on creating and evaluating topic models with Gensim

gensim_topic_modeling.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",
    "### 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",
    "    - Patterns library; Gensim uses this for lemmatization.\n",
    "    - nltk.stopwords\n",
    "    - pyLDAVis\n",
    "We will be playing around with 4 different topic models here:\n",
    "    - LSI (Latent Semantic Indexing)\n",
    "    - HDP (Hierarchical Dirichlet Process)\n",
    "    - LDA (Latent Dirichlet Allocation)\n",
    "    - LDA (tweaked with topic coherence to find optimal number of topics) and\n",
    "    - LDA as LSI with the help of topic coherence metrics\n",
    "First we'll fit those topic models on our existing data then we'll compare each against the other and see how they rank in terms of human interpretability.\n",
    "\n",
    "All can be found in gensim and can be easily used in a plug-and-play fashion. We will tinker with the LDA model using the newly added topic coherence metrics in gensim based on [this](http://svn.aksw.org/papers/2015/WSDM_Topic_Evaluation/public.pdf) paper by Roeder et al and see how the resulting topic model compares with the exsisting ones."
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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",
    "from gensim.models import CoherenceModel, LdaModel, LsiModel, HdpModel\n",
    "from gensim.models.wrappers import LdaMallet\n",
    "from gensim.corpora import Dictionary\n",
    "from pprint import pprint\n",
    "\n",
    "%matplotlib inline"
   ]
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   "outputs": [],
   "source": [
    "test_data_dir = '{}'.format(os.sep).join([gensim.__path__[0], 'test', 'test_data'])\n",
    "lee_train_file = test_data_dir + os.sep + 'lee_background.cor'"
   ]
  },
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   "source": [
    "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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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['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"
     ]
    }
   ],
   "source": [
    "with open(lee_train_file) as f:\n",
    "    for n, l in enumerate(f):\n",
    "        if n < 5:\n",
    "            print([l])"
   ]
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   "source": [
    "def build_texts(fname):\n",
    "    \"\"\"\n",
    "    Function to build tokenized texts from file\n",
    "    \n",
    "    Parameters:\n",
    "    ----------\n",
    "    fname: File to be read\n",
    "    \n",
    "    Returns:\n",
    "    -------\n",
    "    yields preprocessed line\n",
    "    \"\"\"\n",
    "    with open(fname) as f:\n",
    "        for line in f:\n",
    "            yield gensim.utils.simple_preprocess(line, deacc=True, min_len=3)"
   ]
  },
  {
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   "execution_count": 5,
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     "id": "4dbde9d2-3a9d-4677-8dd4-90066c0cb7c4"
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   "outputs": [],
   "source": [
    "train_texts = list(build_texts(lee_train_file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
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     "id": "e7995c4c-f483-4cb5-9f53-3b8c5fef39c3"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_texts)"
   ]
  },
  {
   "cell_type": "markdown",
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     "id": "a6d40ad7-2abd-44e5-8839-62731fb5eab7"
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   "source": [
    "### 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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     "id": "3ca3d45b-a28b-41c7-b5de-4c124c50d13d"
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   "outputs": [],
   "source": [
    "bigram = gensim.models.Phrases(train_texts)  # for bigram collocation detection"
   ]
  },
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'new_york', u'example']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigram[['new', 'york', 'example']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
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     "id": "1181e3a8-6803-4f41-9d55-397f3d700c28"
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   },
   "outputs": [],
   "source": [
    "from gensim.utils import lemmatize\n",
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "dbeeeabe-b6dd-477c-a798-fb7b39302ba9"
    }
   },
   "outputs": [],
   "source": [
    "stops = set(stopwords.words('english'))  # nltk stopwords list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "3d784001-6875-4be5-b8e8-e6c490f5b7b4"
    }
   },
   "outputs": [],
   "source": [
    "def process_texts(texts):\n",
    "    \"\"\"\n",
    "    Function to process texts. Following are the steps we take:\n",
    "    \n",
    "    1. Stopword Removal.\n",
    "    2. Collocation detection.\n",
    "    3. Lemmatization (not stem since stemming can reduce the interpretability).\n",
    "    \n",
    "    Parameters:\n",
    "    ----------\n",
    "    texts: Tokenized texts.\n",
    "    \n",
    "    Returns:\n",
    "    -------\n",
    "    texts: Pre-processed tokenized texts.\n",
    "    \"\"\"\n",
    "    texts = [[word for word in line if word not in stops] for line in texts]\n",
    "    texts = [bigram[line] for line in texts]\n",
    "    texts = [[word.split('/')[0] for word in lemmatize(' '.join(line), allowed_tags=re.compile('(NN)'), min_length=3)] for line in texts]\n",
    "    return texts"
   ]
  },
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   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
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     "id": "d8cfc39f-aa3b-469f-ae34-1ef99ef51a25"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['afghani',\n",
       "  'asylum_seeker',\n",
       "  'australia',\n",
       "  'return',\n",
       "  'home',\n",
       "  'environment',\n",
       "  'government',\n",
       "  'application',\n",
       "  'kabul',\n",
       "  'foreign_affair',\n",
       "  'downer',\n",
       "  'process',\n",
       "  'threat',\n",
       "  'person',\n",
       "  'asylum',\n",
       "  'afghan',\n",
       "  'australia',\n",
       "  'matter',\n",
       "  'britain',\n",
       "  'country',\n",
       "  'europe',\n",
       "  'taliban',\n",
       "  'power',\n",
       "  'afghanistan',\n",
       "  'taliban',\n",
       "  'airlift',\n",
       "  'detainee',\n",
       "  'christmas',\n",
       "  'island',\n",
       "  'island',\n",
       "  'nauru',\n",
       "  'total',\n",
       "  'person',\n",
       "  'island',\n",
       "  'operation',\n",
       "  'aircraft',\n",
       "  'airlift',\n",
       "  'today',\n",
       "  'asylum_seeker',\n",
       "  'claim',\n",
       "  'visa',\n",
       "  'department',\n",
       "  'immigration',\n",
       "  'detainee',\n",
       "  'christmas',\n",
       "  'island',\n",
       "  'spokesman',\n",
       "  'decision']]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_texts = process_texts(train_texts)\n",
    "train_texts[5:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "0e5ca1a8-9c78-412a-9ab4-a4d0be5afd34"
    }
   },
   "source": [
    "Finalising our dictionary and corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
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     "id": "161e8770-8bc2-41ae-98f4-08d1c9311e82"
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   },
   "outputs": [],
   "source": [
    "dictionary = Dictionary(train_texts)\n",
    "corpus = [dictionary.doc2bow(text) for text in train_texts]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "id": "fe809373-e88b-44eb-9cb9-08be3dc5949a"
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   },
   "source": [
    "### Topic modeling with LSI\n",
    "This is a useful topic modeling algorithm in that it can rank topics by itself. Thus it outputs topics in a ranked order. However it does require a `num_topics` parameter (set to 200 by default) to determine the number of latent dimensions after the SVD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "58e7dda6-0dd2-4e4f-b81a-0b530c66b20b"
    }
   },
   "outputs": [],
   "source": [
    "lsimodel = LsiModel(corpus=corpus, num_topics=10, id2word=dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "6b5572c0-2ab0-4b13-a08f-3db21b4c4f21"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  u'-0.241*\"person\" + -0.202*\"australia\" + -0.201*\"government\" + -0.193*\"afghanistan\" + -0.182*\"day\" + -0.174*\"attack\" + -0.156*\"force\" + -0.155*\"area\" + -0.154*\"man\" + -0.147*\"security\"'),\n",
       " (1,\n",
       "  u'0.524*\"fire\" + 0.274*\"sydney\" + 0.269*\"area\" + 0.219*\"firefighter\" + 0.180*\"wale\" + 0.163*\"wind\" + -0.139*\"israel\" + -0.138*\"attack\" + 0.136*\"line\" + 0.126*\"today\"'),\n",
       " (2,\n",
       "  u'-0.333*\"australia\" + 0.320*\"israel\" + 0.243*\"palestinian\" + -0.205*\"afghanistan\" + 0.204*\"fire\" + 0.177*\"attack\" + 0.174*\"sharon\" + 0.128*\"yasser_arafat\" + -0.122*\"company\" + 0.119*\"office\"'),\n",
       " (3,\n",
       "  u'0.353*\"afghanistan\" + -0.301*\"australia\" + 0.236*\"pakistan\" + 0.221*\"force\" + 0.153*\"afghan\" + -0.152*\"test\" + -0.150*\"company\" + 0.146*\"area\" + -0.132*\"union\" + 0.114*\"tora_bora\"'),\n",
       " (4,\n",
       "  u'-0.331*\"union\" + -0.327*\"company\" + 0.197*\"test\" + 0.193*\"australia\" + -0.190*\"worker\" + 0.189*\"day\" + -0.169*\"qanta\" + -0.150*\"pakistan\" + 0.136*\"wicket\" + -0.130*\"commission\"')]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsimodel.show_topics(num_topics=5)  # Showing only the top 5 topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "b1a8c7b4-dc46-4bfe-b17b-d604f212b389"
    }
   },
   "outputs": [],
   "source": [
    "lsitopics = lsimodel.show_topics(formatted=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "943a5fcd-7c2e-4c16-9879-34882a7a74d4"
    }
   },
   "source": [
    "### Topic modeling with [HDP](http://jmlr.csail.mit.edu/proceedings/papers/v15/wang11a/wang11a.pdf)\n",
    "An HDP model is fully unsupervised. It can also determine the ideal number of topics it needs through posterior inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "b492de13-0053-416c-b314-1bbae21ca828"
    }
   },
   "outputs": [],
   "source": [
    "hdpmodel = HdpModel(corpus=corpus, id2word=dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'topic 0: 0.004*collapse + 0.004*afghanistan + 0.004*troop + 0.003*force + 0.003*government + 0.002*benefit + 0.002*operation + 0.002*taliban + 0.002*time + 0.002*today + 0.002*ypre + 0.002*tourism + 0.002*person + 0.002*help + 0.002*wayne + 0.002*fire + 0.002*peru + 0.002*day + 0.002*united_state + 0.002*hih',\n",
       " u'topic 1: 0.003*group + 0.003*government + 0.002*target + 0.002*palestinian + 0.002*end + 0.002*terrorism + 0.002*cease + 0.002*memorandum + 0.002*radio + 0.002*call + 0.002*official + 0.002*path + 0.002*security + 0.002*wayne + 0.002*attack + 0.002*human_right + 0.001*four + 0.001*gunman + 0.001*sharon + 0.001*subsidiary',\n",
       " u'topic 2: 0.003*rafter + 0.003*double + 0.003*team + 0.002*reality + 0.002*manager + 0.002*cup + 0.002*australia + 0.002*abc + 0.002*nomination + 0.002*user + 0.002*freeman + 0.002*herberton + 0.002*lung + 0.002*believe + 0.002*injury + 0.002*steve_waugh + 0.002*fact + 0.002*statement + 0.002*mouth + 0.002*alejandro',\n",
       " u'topic 3: 0.003*india + 0.003*sector + 0.002*anthony + 0.002*interview + 0.002*suicide_bomber + 0.002*union + 0.002*marconi + 0.002*imprisonment + 0.002*document + 0.002*mood + 0.002*remember + 0.002*repair + 0.002*vicki + 0.001*training + 0.001*dressing + 0.001*government + 0.001*indian + 0.001*law + 0.001*convention + 0.001*pair',\n",
       " u'topic 4: 0.003*airport + 0.003*commission + 0.002*marathon + 0.002*tonne + 0.002*citizen + 0.002*dickie + 0.002*arrest + 0.002*taliban + 0.002*opposition + 0.002*agha + 0.002*pitch + 0.002*tune + 0.002*regulation + 0.002*monday + 0.002*chile + 0.002*night + 0.002*foreign_affair + 0.002*charge + 0.002*county + 0.002*signature',\n",
       " u'topic 5: 0.005*company + 0.002*share + 0.002*version + 0.002*entitlement + 0.002*staff + 0.002*value + 0.002*tanzim + 0.002*bay + 0.002*beaumont + 0.002*cent + 0.002*world + 0.002*hass + 0.002*broker + 0.002*line + 0.002*tie + 0.002*plane + 0.002*flare + 0.001*creditor + 0.001*pay + 0.001*administrator',\n",
       " u'topic 6: 0.002*hiv + 0.002*aids + 0.002*margin + 0.002*worker + 0.002*horror + 0.002*claire + 0.002*nation + 0.002*person + 0.002*battleground + 0.002*christmas + 0.002*quarters + 0.002*day + 0.002*underdog + 0.002*festival + 0.002*devaluation + 0.002*immunity + 0.001*quirindi + 0.001*auditor + 0.001*europe + 0.001*board',\n",
       " u'topic 7: 0.002*david + 0.002*victim + 0.002*navy + 0.002*promise + 0.002*symbol + 0.002*site + 0.002*agenda + 0.002*endeavour + 0.002*hamas + 0.002*installation + 0.002*bulli + 0.002*quarrel + 0.002*israeli + 0.002*leaf + 0.002*space + 0.002*sharon + 0.002*spa + 0.002*dispute + 0.002*council + 0.002*tit',\n",
       " u'topic 8: 0.005*storm + 0.004*tree + 0.002*roger + 0.002*aedt + 0.002*minister + 0.002*service + 0.002*sydney + 0.002*electricity + 0.002*power + 0.002*split + 0.002*impact + 0.002*australia + 0.002*area + 0.002*quirindi + 0.002*expansion + 0.002*hornsby + 0.002*standing + 0.002*judgment + 0.002*search + 0.002*thank',\n",
       " u'topic 9: 0.003*australia + 0.003*economy + 0.002*ward + 0.002*game + 0.002*brought + 0.002*johnston + 0.002*supporter + 0.002*recession + 0.002*stray + 0.002*boat_people + 0.002*ritual + 0.002*thousand + 0.001*police + 0.001*box + 0.001*britain + 0.001*year + 0.001*thing + 0.001*kill + 0.001*tour + 0.001*junction',\n",
       " u'topic 10: 0.003*match + 0.003*crowd + 0.002*team + 0.002*rafter + 0.002*scrapping + 0.002*decision + 0.002*guarantee + 0.002*masood + 0.002*tennis + 0.002*forestry + 0.002*world + 0.002*france + 0.002*member + 0.002*career + 0.002*australia + 0.002*single + 0.002*rubber + 0.002*road + 0.002*tower + 0.002*attack',\n",
       " u'topic 11: 0.002*cycle + 0.002*communication + 0.002*spend + 0.002*airline + 0.002*flight + 0.002*amendment + 0.002*swift + 0.002*morning + 0.002*ansett + 0.002*mark + 0.002*platform + 0.002*administrator + 0.002*screen + 0.002*launceston + 0.002*airplane + 0.002*alarming + 0.002*worker + 0.001*tent + 0.001*severance + 0.001*wilton',\n",
       " u'topic 12: 0.003*summit + 0.003*indonesia + 0.002*john + 0.002*pitwater + 0.002*president + 0.002*week + 0.002*howard + 0.002*issue + 0.002*baptist + 0.002*city + 0.002*model + 0.002*mile + 0.002*talk + 0.002*australia + 0.002*network + 0.002*head + 0.002*passage + 0.002*quinlan + 0.002*start + 0.002*match',\n",
       " u'topic 13: 0.002*sorrow + 0.002*australia + 0.002*israelis + 0.002*middle_east + 0.002*deck + 0.002*sydney + 0.002*variety + 0.002*zimbabwean + 0.002*general + 0.002*calculation + 0.002*instrument + 0.002*piece + 0.002*treatment + 0.002*truce + 0.002*wicket + 0.002*submission + 0.002*line + 0.002*december + 0.002*showing + 0.001*father',\n",
       " u'topic 14: 0.002*game + 0.002*giuliani + 0.002*care + 0.002*java + 0.002*mystery + 0.002*session + 0.002*seeker + 0.002*distance + 0.002*tennessee + 0.002*transmission + 0.002*hamid + 0.002*cabinet + 0.002*day + 0.002*regret + 0.002*australia + 0.002*lifestyle + 0.002*afghanistan + 0.002*preview + 0.002*test + 0.002*hit',\n",
       " u'topic 15: 0.003*president + 0.002*rabbani + 0.002*maxi + 0.002*penalty + 0.002*show + 0.002*sibling + 0.002*adjournment + 0.002*new_delhi + 0.002*permission + 0.002*jackie + 0.002*arrest + 0.002*motive + 0.002*outcome + 0.002*shift + 0.002*spy + 0.002*beech + 0.002*beset + 0.002*need + 0.002*personnel + 0.002*mitchell',\n",
       " u'topic 16: 0.002*today + 0.002*matter + 0.002*work + 0.002*debate + 0.002*agreement + 0.002*mastermind + 0.002*member + 0.002*downer + 0.002*intercept + 0.002*bedside + 0.002*felix + 0.002*assembly + 0.002*afghan + 0.002*saudi + 0.002*burn + 0.002*franc + 0.002*modification + 0.002*spelt + 0.002*declared + 0.002*resist',\n",
       " u'topic 17: 0.002*margaret + 0.002*government + 0.002*disruption + 0.002*hingis + 0.002*section + 0.002*security + 0.002*corps + 0.002*pakistan + 0.002*front + 0.002*insurance + 0.002*maintenance + 0.002*order + 0.002*plume + 0.002*amendment + 0.002*demand + 0.001*hawke + 0.001*coal + 0.001*discontent + 0.001*modification + 0.001*distress',\n",
       " u'topic 18: 0.002*speaker + 0.002*love + 0.002*safety + 0.002*chaman + 0.002*coastguard + 0.002*salfit + 0.002*soccer + 0.002*payment + 0.002*complexity + 0.002*personnel + 0.002*flood + 0.002*employment + 0.002*morrow + 0.002*community + 0.002*darren + 0.002*context + 0.001*tunnel + 0.001*negotiation + 0.001*friendship + 0.001*sutherland',\n",
       " u'topic 19: 0.003*brain + 0.003*team + 0.003*olympic + 0.002*cell + 0.002*embryo + 0.002*suburb + 0.002*speaking + 0.002*macfarlane + 0.002*sheet + 0.002*overtime + 0.002*man + 0.002*finding + 0.002*canyon + 0.002*research + 0.002*manhattan + 0.002*brutality + 0.002*spot + 0.002*backdrop + 0.001*pervez + 0.001*sector']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdpmodel.show_topics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "85e46481-0245-448c-b4e2-e0c6e175357c"
    }
   },
   "outputs": [],
   "source": [
    "hdptopics = hdpmodel.show_topics(formatted=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "380ef8b7-6de1-4822-ae30-7120e12b5955"
    }
   },
   "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": 20,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "a02b72fb-0049-4ec3-825f-179e396f3904"
    }
   },
   "outputs": [],
   "source": [
    "ldamodel = LdaModel(corpus=corpus, num_topics=10, id2word=dictionary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "672c009d-3dbc-4a1f-a789-2a0fe78729b9"
    }
   },
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "f7724653-52ef-41e8-aa22-6232be216b08"
    }
   },
   "outputs": [],
   "source": [
    "import pyLDAvis.gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "2c5b03e0-ce0f-4999-8fe1-820a9fe06873"
    }
   },
   "outputs": [],
   "source": [
    "pyLDAvis.enable_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "7da56259-bbf2-4f63-93f6-033833ae4494"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el62341396934228628008573380221\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el62341396934228628008573380221_data = {\"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [6, 7, 10, 8, 1, 9, 3, 4, 2, 5], \"token.table\": {\"Topic\": [4, 4, 1, 2, 4, 5, 7, 8, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 5, 7, 3, 6, 1, 2, 3, 4, 5, 7, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 9, 1, 2, 3, 4, 5, 6, 7, 9, 10, 10, 1, 1, 2, 3, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 3, 9, 4, 1, 2, 4, 3, 7, 10, 10, 2, 5, 1, 1, 2, 4, 6, 8, 9, 10, 4, 7, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 9, 1, 2, 2, 5, 4, 5, 3, 9, 10, 2, 5, 6, 7, 10, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 8, 4, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 6, 1, 2, 3, 8, 10, 3, 4, 5, 2, 3, 4, 7, 8, 9, 10, 3, 2, 10, 4, 10, 5, 6, 2, 3, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 9, 1, 3, 4, 7, 9, 3, 9, 2, 8, 2, 5, 6, 7, 8, 10, 2, 1, 6, 1, 2, 3, 4, 6, 7, 8, 9, 10, 3, 1, 5, 4, 3, 3, 7, 9, 10, 1, 3, 8, 9, 10, 4, 3, 7, 10, 1, 2, 5, 6, 8, 9, 8, 6, 7, 2, 4, 7, 8, 6, 1, 2, 3, 4, 5, 6, 7, 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      "text/plain": [
       "PreparedData(topic_coordinates=            Freq  cluster  topics         x         y\n",
       "topic                                                \n",
       "5      15.818395        1       1  0.020284 -0.003740\n",
       "6      12.159659        1       2 -0.022615  0.092623\n",
       "9      11.567771        1       3  0.095137 -0.056635\n",
       "7      10.030019        1       4 -0.041509 -0.014137\n",
       "0       9.723180        1       5 -0.093849 -0.047330\n",
       "8       9.499227        1       6  0.003548  0.063112\n",
       "2       9.403690        1       7 -0.033738 -0.046082\n",
       "3       8.338908        1       8  0.011367 -0.027304\n",
       "1       6.964024        1       9  0.038242  0.009804\n",
       "4       6.495127        1      10  0.023133  0.029689, topic_info=     Category        Freq          Term       Total  loglift  logprob\n",
       "term                                                                 \n",
       "965   Default   69.000000   afghanistan   69.000000  30.0000  30.0000\n",
       "1918  Default   46.000000        israel   46.000000  29.0000  29.0000\n",
       "2464  Default  112.000000     australia  112.000000  28.0000  28.0000\n",
       "3280  Default   33.000000        afghan   33.000000  27.0000  27.0000\n",
       "2354  Default   67.000000          fire   67.000000  26.0000  26.0000\n",
       "1458  Default   38.000000      pakistan   38.000000  25.0000  25.0000\n",
       "757   Default   38.000000   palestinian   38.000000  24.0000  24.0000\n",
       "851   Default   37.000000          test   37.000000  23.0000  23.0000\n",
       "574   Default   51.000000         union   51.000000  22.0000  22.0000\n",
       "1922  Default   59.000000       company   59.000000  21.0000  21.0000\n",
       "1853  Default   18.000000         qanta   18.000000  20.0000  20.0000\n",
       "3333  Default   99.000000    government   99.000000  19.0000  19.0000\n",
       "593   Default   44.000000         child   44.000000  18.0000  18.0000\n",
       "157   Default   29.000000         india   29.000000  17.0000  17.0000\n",
       "1201  Default   13.000000         space   13.000000  16.0000  16.0000\n",
       "545   Default   20.000000  south_africa   20.000000  15.0000  15.0000\n",
       "1462  Default   12.000000        cancer   12.000000  14.0000  14.0000\n",
       "2195  Default    8.000000         virus    8.000000  13.0000  13.0000\n",
       "2060  Default   15.000000         event   15.000000  12.0000  12.0000\n",
       "589   Default   27.000000        worker   27.000000  11.0000  11.0000\n",
       "2735  Default   24.000000         metre   24.000000  10.0000  10.0000\n",
       "3430  Default   30.000000    commission   30.000000   9.0000   9.0000\n",
       "1553  Default   26.000000      director   26.000000   8.0000   8.0000\n",
       "985   Default   23.000000     agreement   23.000000   7.0000   7.0000\n",
       "2549  Default   20.000000        wicket   20.000000   6.0000   6.0000\n",
       "1844  Default   20.000000          rate   20.000000   5.0000   5.0000\n",
       "1377  Default   12.000000       dispute   12.000000   4.0000   4.0000\n",
       "2605  Default   28.000000         peace   28.000000   3.0000   3.0000\n",
       "2989  Default   67.000000        attack   67.000000   2.0000   2.0000\n",
       "1149  Default    6.000000        farmer    6.000000   1.0000   1.0000\n",
       "...       ...         ...           ...         ...      ...      ...\n",
       "1214  Topic10    1.961223   possibility    5.635690   1.6786  -6.3127\n",
       "2269  Topic10    2.441073      harrison    7.646867   1.5923  -6.0938\n",
       "1553  Topic10    5.589432      director   26.667974   1.1715  -5.2654\n",
       "991   Topic10    4.603794        change   21.175656   1.2081  -5.4594\n",
       "1303  Topic10    1.659603       premier    4.774002   1.6775  -6.4797\n",
       "3289  Topic10    3.672805        weapon   15.957912   1.2651  -5.6853\n",
       "2840  Topic10    2.639075       tension   10.503602   1.3528  -6.0159\n",
       "2263  Topic10    3.590576         river   18.683428   1.0848  -5.7080\n",
       "1140  Topic10    6.490434         group   58.260255   0.5395  -5.1160\n",
       "2319  Topic10    3.892480     community   24.092294   0.9113  -5.6272\n",
       "1922  Topic10    6.169639       company   59.473519   0.4682  -5.1666\n",
       "1584  Topic10    4.137423         death   27.575597   0.8373  -5.5662\n",
       "157   Topic10    4.256485         india   29.854661   0.7862  -5.5378\n",
       "2422  Topic10    8.052329        person  125.100187  -0.0090  -4.9003\n",
       "949   Topic10    4.045679          team   31.873346   0.6700  -5.5886\n",
       "1893  Topic10    3.870061        action   32.053391   0.6200  -5.6330\n",
       "2344  Topic10    4.239250       meeting   42.681139   0.4247  -5.5419\n",
       "2605  Topic10    3.525193         peace   28.066990   0.6595  -5.7264\n",
       "2464  Topic10    5.517708     australia  112.829012  -0.2838  -5.2783\n",
       "3165  Topic10    3.623557        police   34.081096   0.4928  -5.6988\n",
       "1608  Topic10    3.922546         world   45.568210   0.2816  -5.6195\n",
       "194   Topic10    3.747631     president   44.732037   0.2546  -5.6652\n",
       "3333  Topic10    4.302260    government   99.282266  -0.4047  -5.5271\n",
       "546   Topic10    3.148621       country   30.045861   0.4784  -5.8393\n",
       "2307  Topic10    3.397572  united_state   47.946180   0.0871  -5.7632\n",
       "1931  Topic10    3.315484          hour   47.117280   0.0801  -5.7877\n",
       "2989  Topic10    3.477635        attack   67.204296  -0.2273  -5.7399\n",
       "1110  Topic10    3.375391           man   70.001434  -0.2979  -5.7698\n",
       "2904  Topic10    3.383324           day   91.814892  -0.5668  -5.7674\n",
       "1692  Topic10    3.248350          area   67.475462  -0.2995  -5.8081\n",
       "\n",
       "[756 rows x 6 columns], token_table=      Topic      Freq       Term\n",
       "term                            \n",
       "311       4  0.821215     abbott\n",
       "1059      4  0.619885   abortion\n",
       "2850      1  0.071487      abuse\n",
       "2850      2  0.285947      abuse\n",
       "2850      4  0.357434      abuse\n",
       "2850      5  0.071487      abuse\n",
       "2850      7  0.071487      abuse\n",
       "2850      8  0.071487      abuse\n",
       "2850     10  0.142973      abuse\n",
       "1893      1  0.062396     action\n",
       "1893      2  0.093594     action\n",
       "1893      3  0.093594     action\n",
       "1893      4  0.187188     action\n",
       "1893      5  0.062396     action\n",
       "1893      6  0.062396     action\n",
       "1893      7  0.062396     action\n",
       "1893      8  0.155990     action\n",
       "1893      9  0.093594     action\n",
       "1893     10  0.124792     action\n",
       "844       5  0.212931      actor\n",
       "844       7  0.425862      actor\n",
       "1719      3  0.804982  advantage\n",
       "1173      6  0.614371     advent\n",
       "3280      1  0.270599     afghan\n",
       "3280      2  0.090200     afghan\n",
       "3280      3  0.030067     afghan\n",
       "3280      4  0.030067     afghan\n",
       "3280      5  0.450999     afghan\n",
       "3280      7  0.030067     afghan\n",
       "3280      9  0.030067     afghan\n",
       "...     ...       ...        ...\n",
       "1192      4  0.031473       year\n",
       "1192      5  0.157364       year\n",
       "1192      6  0.110155       year\n",
       "1192      7  0.141628       year\n",
       "1192      8  0.078682       year\n",
       "1192      9  0.078682       year\n",
       "1192     10  0.047209       year\n",
       "1166      1  0.234873   year_old\n",
       "1166      2  0.039145   year_old\n",
       "1166      3  0.039145   year_old\n",
       "1166      5  0.156582   year_old\n",
       "1166      6  0.195727   year_old\n",
       "1166      7  0.078291   year_old\n",
       "1166      8  0.156582   year_old\n",
       "1166      9  0.039145   year_old\n",
       "1166     10  0.039145   year_old\n",
       "2408      1  0.165536  yesterday\n",
       "2408      2  0.094592  yesterday\n",
       "2408      3  0.165536  yesterday\n",
       "2408      4  0.094592  yesterday\n",
       "2408      5  0.118240  yesterday\n",
       "2408      6  0.070944  yesterday\n",
       "2408      7  0.118240  yesterday\n",
       "2408      8  0.094592  yesterday\n",
       "2408      9  0.047296  yesterday\n",
       "2408     10  0.047296  yesterday\n",
       "238       9  0.421611  zaccarias\n",
       "3339      1  0.261213   zimbabwe\n",
       "3339      4  0.261213   zimbabwe\n",
       "3339     10  0.522426   zimbabwe\n",
       "\n",
       "[2006 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[6, 7, 10, 8, 1, 9, 3, 4, 2, 5])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.gensim.prepare(ldamodel, corpus, dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "13f462fd-b0c3-4a29-90f3-050e2317b72c"
    }
   },
   "outputs": [],
   "source": [
    "ldatopics = ldamodel.show_topics(formatted=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "68d38063-fb92-4d36-b3fb-f2d3cbff9eb5"
    }
   },
   "source": [
    "### Finding out the optimal number of topics\n",
    "__Introduction to topic coherence__:\n",
    "<img src=\"https://rare-technologies.com/wp-content/uploads/2016/06/pipeline.png\">\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": 25,
   "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": 26,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "e8936716-d06c-4cef-ae87-5c5da9a25a85"
    }
   },
   "outputs": [
    {
     "data": {
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OIWwGbAQ0DiG0DiEMSzWIEMKjeHLTF5gM7AwcGH6f5LsZqxauNsQTiqnAWOAo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\nmJNOgi5doo5GJP8oERGRrIjHvU5kxYq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RwKCIqFQNraTxwJsRcXpN2ThgSUScUi6y5pK0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\npybxyV0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0cea715390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 22.8 s, sys: 536 ms, total: 23.4 s\n",
      "Wall time: 22.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "lmlist, c_v = evaluate_graph(dictionary=dictionary, corpus=corpus, texts=train_texts, limit=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "c02b6f0a-801c-4a8f-aa8f-8c9d64df7a9a"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el6234139693600051664982684879\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el6234139693600051664982684879_data = {\"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [2, 1, 3], \"token.table\": {\"Topic\": [2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 1, 2, 3, 2, 3, 3, 1, 2, 3, 1, 2, 3, 1, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 3, 2, 2, 1, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 3, 1, 2, 3, 1, 1, 2, 3, 2, 2, 3, 3, 1, 3, 1, 2, 3, 1, 2, 3, 2, 3, 1, 2, 3, 1, 2, 3, 1, 3, 1, 3, 1, 2, 3, 1, 3, 1, 2, 3, 2, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 1, 2, 3, 1, 3, 1, 3, 1, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 1, 2, 3, 1, 3, 1, 3, 2, 3, 1, 3, 1, 2, 3, 1, 2, 3, 2, 1, 2, 3, 3, 1, 2, 3, 1, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 2, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 1, 2, 3, 1, 1, 2, 3, 1, 1, 2, 3, 1, 2, 3, 1, 3, 1, 2, 3, 1, 2, 3, 3, 1, 2, 3, 1, 2, 3, 3, 1, 2, 3, 2, 2, 1, 2, 3, 1, 2, 3, 2, 1, 2, 1, 2, 1, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 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-5.4305000000000003, -5.9130000000000003, -6.0944000000000003, -5.6273999999999997, -5.6218000000000004, -4.8787000000000003, -5.0289000000000001, -6.2337999999999996, -5.8593000000000002, -5.3436000000000003, -6.0632999999999999, -5.7064000000000004, -5.1951999999999998, -6.0364000000000004, -5.8369, -5.7129000000000003, -5.726, -5.9744999999999999, -5.5598999999999998, -5.5942999999999996, -5.6965000000000003, -5.5923999999999996, -5.8658000000000001, -5.7351999999999999, -5.6836000000000002, -5.6841999999999997, -5.7060000000000004, -5.8539000000000003, -5.8667999999999996, -7.2518000000000002, -7.5307000000000004, -7.3323999999999998, -7.5629999999999997, -7.5635000000000003, -7.8722000000000003, -7.8764000000000003, -7.8765999999999998, -7.5895999999999999, -7.8823999999999996, -6.7912999999999997, -7.5999999999999996, -7.6100000000000003, -7.9010999999999996, -7.6425999999999998, -7.2618, -7.9561999999999999, -6.7695999999999996, -7.3029999999999999, -7.7019000000000002, -7.6992000000000003, -7.9916999999999998, -7.9987000000000004, -7.2881999999999998, -6.8000999999999996, -8.0165000000000006, -7.7416, -6.8048000000000002, -8.4278999999999993, -8.4281000000000006, -6.4238999999999997, -7.5137999999999998, -7.0513000000000003, -6.1214000000000004, -7.2065000000000001, -6.9558999999999997, -5.6025999999999998, -6.2813999999999997, -5.8945999999999996, -6.3274999999999997, -5.2119, -5.6261999999999999, -5.9360999999999997, -6.2878999999999996, -6.0125000000000002, -5.7464000000000004, -6.1460999999999997, -5.9996, -5.2770999999999999, -5.7257999999999996, -5.5029000000000003, -6.3719999999999999, -6.5541999999999998, -5.1874000000000002, -6.0327999999999999, -5.7241, -5.6928999999999998, -6.3348000000000004, -5.5301999999999998, -5.2439, -5.5198, -5.7469999999999999, -5.5861999999999998, -5.6161000000000003, -5.8212999999999999, -5.9931999999999999, -5.6096000000000004, -5.7367999999999997, -5.4119999999999999, -5.4535999999999998, -5.7668999999999997, -5.9603000000000002, -5.9013, -6.0232000000000001, -7.4928999999999997, -7.4931000000000001, -7.3223000000000003, -7.3415999999999997, -7.8578000000000001, -7.8619000000000003, -7.1862000000000004, -7.1780999999999997, -7.8935000000000004, -7.6151999999999997, -7.6140999999999996, -7.9089, -6.5263, -7.9157999999999999, -7.9260999999999999, -7.6489000000000003, -7.9549000000000003, -7.9550000000000001, -7.6708999999999996, -7.9679000000000002, -7.1422999999999996, -7.9885999999999999, -7.4652000000000003, -7.9965999999999999, -7.1559999999999997, -7.9931999999999999, -8.0040999999999993, -8.4082000000000008, -8.4083000000000006, -8.4084000000000003, -7.7107999999999999, -6.9043000000000001, -7.4970999999999997, -6.9493, -5.4366000000000003, -6.8783000000000003, -6.9987000000000004, -5.6573000000000002, -6.843, -7.0046999999999997, -6.9279000000000002, -5.5273000000000003, -6.1772, -4.7614999999999998, -5.7138999999999998, -6.1943000000000001, -5.5201000000000002, -5.6529999999999996, -5.3239000000000001, -6.1722999999999999, -5.8262, -6.1566999999999998, -6.5118, -6.4351000000000003, -6.1047000000000002, -5.3776000000000002, -5.6317000000000004, -5.3817000000000004, -5.4417999999999997, -5.9029999999999996, -5.4353999999999996, -5.2289000000000003, -5.2069000000000001, -6.3699000000000003, -6.3322000000000003, -6.0232000000000001, -5.9645000000000001, -5.7991999999999999, -5.9888000000000003, -6.1228999999999996, -5.6279000000000003, -5.7961, -5.7550999999999997, -5.7869000000000002, -5.7152000000000003, -5.7980999999999998, -5.7134999999999998, -5.9303999999999997, -5.6002999999999998, -5.9085999999999999]}};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el6234139693600051664982684879\", ldavis_el6234139693600051664982684879_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el6234139693600051664982684879\", ldavis_el6234139693600051664982684879_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el6234139693600051664982684879\", ldavis_el6234139693600051664982684879_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=            Freq  cluster  topics         x         y\n",
       "topic                                                \n",
       "1      43.675712        1       1  0.030435 -0.011894\n",
       "0      28.636818        1       2 -0.026509 -0.018098\n",
       "2      27.687471        1       3 -0.003926  0.029991, topic_info=     Category        Freq          Term       Total  loglift  logprob\n",
       "term                                                                 \n",
       "851   Default   37.000000          test   37.000000  30.0000  30.0000\n",
       "3437  Default   25.000000       airline   25.000000  29.0000  29.0000\n",
       "2422  Default  129.000000        person  129.000000  28.0000  28.0000\n",
       "1140  Default   56.000000         group   56.000000  27.0000  27.0000\n",
       "593   Default   43.000000         child   43.000000  26.0000  26.0000\n",
       "1853  Default   18.000000         qanta   18.000000  25.0000  25.0000\n",
       "757   Default   35.000000   palestinian   35.000000  24.0000  24.0000\n",
       "3183  Default   33.000000          wale   33.000000  23.0000  23.0000\n",
       "3044  Default   36.000000        centre   36.000000  22.0000  22.0000\n",
       "2351  Default   23.000000        flight   23.000000  21.0000  21.0000\n",
       "1931  Default   46.000000          hour   46.000000  20.0000  20.0000\n",
       "2464  Default  116.000000     australia  116.000000  19.0000  19.0000\n",
       "661   Default   17.000000      detainee   17.000000  18.0000  18.0000\n",
       "2457  Default   48.000000        sydney   48.000000  17.0000  17.0000\n",
       "2127  Default   25.000000      militant   25.000000  16.0000  16.0000\n",
       "325   Default    9.000000       refugee    9.000000  15.0000  15.0000\n",
       "2873  Default   11.000000        gunman   11.000000  14.0000  14.0000\n",
       "3143  Default   41.000000         state   41.000000  13.0000  13.0000\n",
       "2549  Default   19.000000        wicket   19.000000  12.0000  12.0000\n",
       "949   Default   32.000000          team   32.000000  11.0000  11.0000\n",
       "102   Default   16.000000         hamas   16.000000  10.0000  10.0000\n",
       "2111  Default   34.000000        number   34.000000   9.0000   9.0000\n",
       "2195  Default    9.000000         virus    9.000000   8.0000   8.0000\n",
       "1547  Default   36.000000          week   36.000000   7.0000   7.0000\n",
       "3254  Default    7.000000       woomera    7.000000   6.0000   6.0000\n",
       "2989  Default   68.000000        attack   68.000000   5.0000   5.0000\n",
       "965   Default   68.000000   afghanistan   68.000000   4.0000   4.0000\n",
       "1458  Default   40.000000      pakistan   40.000000   3.0000   3.0000\n",
       "109   Default   16.000000          army   16.000000   2.0000   2.0000\n",
       "1132  Default   25.000000       polouse   25.000000   1.0000   1.0000\n",
       "...       ...         ...           ...         ...      ...      ...\n",
       "791    Topic3   13.599766           way   33.653033   0.3781  -5.8262\n",
       "457    Topic3    9.772432         radio   21.961201   0.4745  -6.1567\n",
       "3469   Topic3    6.851469       problem   13.763973   0.5866  -6.5118\n",
       "1694   Topic3    7.397502         start   15.259331   0.5601  -6.4351\n",
       "142    Topic3   10.293808          work   23.795675   0.4462  -6.1047\n",
       "1038   Topic3   21.297940         today   63.918948   0.1852  -5.3776\n",
       "1931   Topic3   16.519764          hour   46.137796   0.2571  -5.6317\n",
       "1692   Topic3   21.211328          area   65.779869   0.1524  -5.3817\n",
       "884    Topic3   19.973123          time   61.517598   0.1593  -5.4418\n",
       "949    Topic3   12.593494          team   32.284527   0.3428  -5.9030\n",
       "2354   Topic3   20.102475          fire   63.934883   0.1272  -5.4354\n",
       "2904   Topic3   24.711735           day   91.674760  -0.0268  -5.2289\n",
       "3333   Topic3   25.261699    government   98.056135  -0.0721  -5.2069\n",
       "2169   Topic3    7.895483     new_south   17.051104   0.5143  -6.3699\n",
       "1853   Topic3    8.198638         qanta   18.098547   0.4923  -6.3322\n",
       "1346   Topic3   11.167314      per_cent   29.136717   0.3252  -6.0232\n",
       "3087   Topic3   11.842532         staff   32.048978   0.2886  -5.9645\n",
       "2408   Topic3   13.971238     yesterday   41.805680   0.1882  -5.7992\n",
       "1494   Topic3   11.557994      minister   31.327486   0.2871  -5.9888\n",
       "3437   Topic3   10.107526       airline   25.268230   0.3679  -6.1229\n",
       "713    Topic3   16.582083      security   56.237078   0.0629  -5.6279\n",
       "2344   Topic3   14.015395       meeting   43.123292   0.1603  -5.7961\n",
       "1608   Topic3   14.601073         world   46.283007   0.1305  -5.7551\n",
       "2307   Topic3   14.144147  united_state   47.424821   0.0743  -5.7869\n",
       "1922   Topic3   15.195149       company   61.156859  -0.1083  -5.7152\n",
       "41     Topic3   13.987025         force   53.790616  -0.0628  -5.7981\n",
       "1110   Topic3   15.221356           man   70.658805  -0.2510  -5.7135\n",
       "3382   Topic3   12.253432        leader   42.187497   0.0479  -5.9304\n",
       "2422   Topic3   17.046831        person  129.542161  -0.7439  -5.6003\n",
       "574    Topic3   12.523667         union   52.557544  -0.1501  -5.9086\n",
       "\n",
       "[259 rows x 6 columns], token_table=      Topic      Freq            Term\n",
       "term                                 \n",
       "1059      2  0.631342        abortion\n",
       "1059      3  0.315671        abortion\n",
       "1893      1  0.536429          action\n",
       "1893      2  0.238413          action\n",
       "1893      3  0.208611          action\n",
       "3280      1  0.622198          afghan\n",
       "3280      2  0.217769          afghan\n",
       "3280      3  0.186660          afghan\n",
       "965       1  0.700378     afghanistan\n",
       "965       2  0.145912     afghanistan\n",
       "965       3  0.145912     afghanistan\n",
       "985       1  0.612333       agreement\n",
       "985       2  0.174952       agreement\n",
       "985       3  0.218690       agreement\n",
       "3437      1  0.118726         airline\n",
       "3437      2  0.514480         airline\n",
       "3437      3  0.395754         airline\n",
       "2063      1  0.312281         alcohol\n",
       "2063      2  0.624561         alcohol\n",
       "207       1  0.249899       ambulance\n",
       "207       2  0.749696       ambulance\n",
       "207       3  0.249899       ambulance\n",
       "1957      2  0.838777       amendment\n",
       "2358      3  0.628736         anthony\n",
       "1841      3  0.836923      antibiotic\n",
       "1692      1  0.410460            area\n",
       "1692      2  0.258438            area\n",
       "1692      3  0.319247            area\n",
       "109       1  0.307961            army\n",
       "109       2  0.554330            army\n",
       "...     ...       ...             ...\n",
       "3183      3  0.481597            wale\n",
       "794       1  0.433821             war\n",
       "794       2  0.371847             war\n",
       "794       3  0.185923             war\n",
       "791       1  0.416010             way\n",
       "791       2  0.178290             way\n",
       "791       3  0.416010             way\n",
       "1547      1  0.388289            week\n",
       "1547      2  0.194145            week\n",
       "1547      3  0.416024            week\n",
       "2549      1  0.200089          wicket\n",
       "2549      2  0.300133          wicket\n",
       "2549      3  0.500222          wicket\n",
       "3254      1  0.139154         woomera\n",
       "3254      2  0.695771         woomera\n",
       "3254      3  0.139154         woomera\n",
       "142       1  0.420244            work\n",
       "142       2  0.168098            work\n",
       "142       3  0.420244            work\n",
       "1608      1  0.453730           world\n",
       "1608      2  0.237668           world\n",
       "1608      3  0.324093           world\n",
       "2073      1  0.691764  world_heritage\n",
       "144       1  0.860906            worm\n",
       "1192      1  0.440823            year\n",
       "1192      2  0.204668            year\n",
       "1192      3  0.346361            year\n",
       "2408      1  0.382723       yesterday\n",
       "2408      2  0.287042       yesterday\n",
       "2408      3  0.334883       yesterday\n",
       "\n",
       "[434 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[2, 1, 3])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.gensim.prepare(lmlist[2], corpus, dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "699e2ddb-add6-4134-a723-1bb37029f013"
    }
   },
   "outputs": [],
   "source": [
    "lmtopics = lmlist[5].show_topics(formatted=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "caa35df4-b625-4246-bc1f-42c561f31486"
    }
   },
   "source": [
    "### LDA as LSI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "d2aa5aac-acc7-41c9-96d4-5410c0d8a14b"
    }
   },
   "source": [
    "One of the problem with LDA is that if we train it on a large number of topics, the topics get \"lost\" among the numbers. Let us see if we can dig out the best topics from the best LDA model we can produce. The function below can be used to control the quality of the LDA model we produce."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "c39406c8-1e69-4249-91ac-894338d4053b"
    }
   },
   "outputs": [],
   "source": [
    "def ret_top_model():\n",
    "    \"\"\"\n",
    "    Since LDAmodel is a probabilistic model, it comes up different topics each time we run it. To control the\n",
    "    quality of the topic model we produce, we can see what the interpretability of the best topic is and keep\n",
    "    evaluating the topic model until this threshold is crossed. \n",
    "    \n",
    "    Returns:\n",
    "    -------\n",
    "    lm: Final evaluated topic model\n",
    "    top_topics: ranked topics in decreasing order. List of tuples\n",
    "    \"\"\"\n",
    "    top_topics = [(0, 0)]\n",
    "    while top_topics[0][1] < 0.97:\n",
    "        lm = LdaModel(corpus=corpus, id2word=dictionary)\n",
    "        coherence_values = {}\n",
    "        for n, topic in lm.show_topics(num_topics=-1, formatted=False):\n",
    "            topic = [word for word, _ in topic]\n",
    "            cm = CoherenceModel(topics=[topic], texts=train_texts, dictionary=dictionary, window_size=10)\n",
    "            coherence_values[n] = cm.get_coherence()\n",
    "        top_topics = sorted(coherence_values.items(), key=operator.itemgetter(1), reverse=True)\n",
    "    return lm, top_topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "6b8eef4a-a87d-42bd-84dd-586610828698"
    }
   },
   "outputs": [],
   "source": [
    "lm, top_topics = ret_top_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "43870992-c2ff-47cf-8b6b-f62855673b42"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(91, 0.99286550077029223), (42, 0.96031455145699274), (54, 0.87011963575683104), (2, 0.84575428129030361), (10, 0.83238343784453017)]\n"
     ]
    }
   ],
   "source": [
    "print(top_topics[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inference\n",
    "We can clearly see below that the first topic is about __cinema__, second is about __email malware__, third is about the land which was given back to the __Larrakia aboriginal community of Australia__ in 2000. Then there's one about __Australian cricket__. LDA as LSI has worked wonderfully in finding out the best topics from within LDA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "281d1434-ce22-46c6-ba53-71a24a476568"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[(u'actor', 0.034688196735986693),\n",
      "  (u'picture', 0.023163878883499418),\n",
      "  (u'award', 0.023163878883499418),\n",
      "  (u'comedy', 0.023163878883499418),\n",
      "  (u'globe', 0.023163878883499418),\n",
      "  (u'nomination', 0.023163878883499418),\n",
      "  (u'actress', 0.023163878883499418),\n",
      "  (u'film', 0.023163878883499418),\n",
      "  (u'drama', 0.011639561031012149),\n",
      "  (u'winner', 0.011639561031012149)],\n",
      " [(u'virus', 0.064292949289013482),\n",
      "  (u'user', 0.048074573973209883),\n",
      "  (u'computer', 0.040350900997751814),\n",
      "  (u'company', 0.028173623478117912),\n",
      "  (u'email', 0.022580226976870982),\n",
      "  (u'worm', 0.020928236506996975),\n",
      "  (u'attachment', 0.014534311779706417),\n",
      "  (u'outlook', 0.01260706654637953),\n",
      "  (u'software', 0.011909411409069969),\n",
      "  (u'list', 0.0088116041533348403)],\n",
      " [(u'claim', 0.0096511365969504694),\n",
      "  (u'agreement', 0.0082836950379963047),\n",
      "  (u'hectare', 0.0077564979304569235),\n",
      "  (u'larrakia', 0.0065928813973845394),\n",
      "  (u'rosebury', 0.006086042494624749),\n",
      "  (u'term', 0.004880655853124416),\n",
      "  (u'region', 0.004786636929111303),\n",
      "  (u'title', 0.0045026307214029735),\n",
      "  (u'palmerston', 0.0043726827115423677),\n",
      "  (u'developer', 0.0040102561358092521)],\n",
      " [(u'government', 0.046880132726190141),\n",
      "  (u'razor', 0.035772624674521684),\n",
      "  (u'gang', 0.034958865711441162),\n",
      "  (u'minister', 0.023615858300345904),\n",
      "  (u'interest', 0.023531518290467797),\n",
      "  (u'taxpayer', 0.023484887279677492),\n",
      "  (u'nelson', 0.023408331025582648),\n",
      "  (u'spending', 0.023363131530296326),\n",
      "  (u'program', 0.022809499664362586),\n",
      "  (u'colleague', 0.012039863390851384)],\n",
      " [(u'australia', 0.019022701887671096),\n",
      "  (u'outlook', 0.012806577991883974),\n",
      "  (u'price', 0.012017645637892888),\n",
      "  (u'growth', 0.011021360611214826),\n",
      "  (u'world', 0.010586500333515535),\n",
      "  (u'imf', 0.0074848683800558145),\n",
      "  (u'half', 0.0073080219523406773),\n",
      "  (u'release', 0.0073069514968024446),\n",
      "  (u'oil', 0.0071307771829650724),\n",
      "  (u'weakening', 0.0067585126681211785)],\n",
      " [(u'role', 0.036823234375415084),\n",
      "  (u'heart', 0.018676496748175567),\n",
      "  (u'mcreddie', 0.018520830095514161),\n",
      "  (u'sir', 0.018430691138823303),\n",
      "  (u'actor', 0.018423768093119148),\n",
      "  (u'attack', 0.018421603513127272),\n",
      "  (u'minister', 0.018330977218667187),\n",
      "  (u'cancer', 0.018246768643902407),\n",
      "  (u'servant', 0.018246520413261125),\n",
      "  (u'friend', 0.018230140539399531)],\n",
      " [(u'australia', 0.038230610979973961),\n",
      "  (u'test', 0.03039802044037989),\n",
      "  (u'day', 0.026478028361575149),\n",
      "  (u'adam', 0.023237227270639361),\n",
      "  (u'wicket', 0.018060239149805601),\n",
      "  (u'match', 0.015652900511647725),\n",
      "  (u'gilchrist', 0.015206348827236857),\n",
      "  (u'steve_waugh', 0.01496754571623464),\n",
      "  (u'south_africa', 0.013902623982144873),\n",
      "  (u'selector', 0.012332915474867073)],\n",
      " [(u'product', 0.067729999063555119),\n",
      "  (u'food', 0.033921347284742248),\n",
      "  (u'consumer', 0.033921347284742241),\n",
      "  (u'company', 0.033921347284742241),\n",
      "  (u'hooke', 0.022651796691804622),\n",
      "  (u'law', 0.022651796691804622),\n",
      "  (u'grocery', 0.022651796691804622),\n",
      "  (u'technology', 0.022651796691804622),\n",
      "  (u'sultan', 0.014079780537934588),\n",
      "  (u'stage', 0.013736597864617922)],\n",
      " [(u'credit', 0.020223411999648302),\n",
      "  (u'way', 0.017706515460000523),\n",
      "  (u'bank', 0.017459639386736926),\n",
      "  (u'card', 0.016308335204832106),\n",
      "  (u'consumer', 0.014565787979687885),\n",
      "  (u'reserve_bank', 0.014365008462949415),\n",
      "  (u'association', 0.011448453247788988),\n",
      "  (u'rate', 0.010363334709658676),\n",
      "  (u'movement', 0.010204675471073506),\n",
      "  (u'inquiry', 0.0093452022355641085)],\n",
      " [(u'fire', 0.045611922604745642),\n",
      "  (u'area', 0.021994719721821848),\n",
      "  (u'firefighter', 0.018748173264525044),\n",
      "  (u'sydney', 0.016599279291396325),\n",
      "  (u'wind', 0.014270025525472343),\n",
      "  (u'property', 0.0098028785236429564),\n",
      "  (u'hour', 0.0097079779464512347),\n",
      "  (u'today', 0.0093953004964965076),\n",
      "  (u'year', 0.0089216764257795157),\n",
      "  (u'state', 0.0086116373269496185)]]\n"
     ]
    }
   ],
   "source": [
    "pprint([lm.show_topic(topicid) for topicid, c_v in top_topics[:10]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "ada914a2-bacb-4300-8ce3-f1332843d24c"
    }
   },
   "outputs": [],
   "source": [
    "lda_lsi_topics = [[word for word, prob in lm.show_topic(topicid)] for topicid, c_v in top_topics]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "4f313370-b5f0-4754-9744-157da0447fc4"
    }
   },
   "source": [
    "### Evaluating all the topic models\n",
    "Any topic model which can come up with topic terms can be plugged into the coherence pipeline. You can even plug in an [NMF topic model](http://derekgreene.com/nmf-topic/) created with scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "4329af31-21b4-4570-903a-6df3715244c7"
    }
   },
   "outputs": [],
   "source": [
    "lsitopics = [[word for word, prob in topic] for topicid, topic in lsitopics]\n",
    "\n",
    "hdptopics = [[word for word, prob in topic] for topicid, topic in hdptopics]\n",
    "\n",
    "ldatopics = [[word for word, prob in topic] for topicid, topic in ldatopics]\n",
    "\n",
    "lmtopics = [[word for word, prob in topic] for topicid, topic in lmtopics]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "0924faf0-a957-4d12-b35c-b50ebb30f370"
    }
   },
   "outputs": [],
   "source": [
    "lsi_coherence = CoherenceModel(topics=lsitopics[:10], texts=train_texts, dictionary=dictionary, window_size=10).get_coherence()\n",
    "\n",
    "hdp_coherence = CoherenceModel(topics=hdptopics[:10], texts=train_texts, dictionary=dictionary, window_size=10).get_coherence()\n",
    "\n",
    "lda_coherence = CoherenceModel(topics=ldatopics, texts=train_texts, dictionary=dictionary, window_size=10).get_coherence()\n",
    "\n",
    "lm_coherence = CoherenceModel(topics=lmtopics, texts=train_texts, dictionary=dictionary, window_size=10).get_coherence()\n",
    "\n",
    "lda_lsi_coherence = CoherenceModel(topics=lda_lsi_topics[:10], texts=train_texts, dictionary=dictionary, window_size=10).get_coherence()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "59cc671f-041a-4d6f-a609-f1b9855aa05c"
    }
   },
   "outputs": [],
   "source": [
    "def evaluate_bar_graph(coherences, indices):\n",
    "    \"\"\"\n",
    "    Function to plot bar graph.\n",
    "    \n",
    "    coherences: list of coherence values\n",
    "    indices: Indices to be used to mark bars. Length of this and coherences should be equal.\n",
    "    \"\"\"\n",
    "    assert len(coherences) == len(indices)\n",
    "    n = len(coherences)\n",
    "    x = np.arange(n)\n",
    "    plt.bar(x, coherences, width=0.2, tick_label=indices, align='center')\n",
    "    plt.xlabel('Models')\n",
    "    plt.ylabel('Coherence Value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "b86ba0c1-c7c4-43a5-a9f5-8dfe7967be6c"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3k1TDPIrbfm9sat8IzG/T51qKUYwfAz3APwKXRcSjM/OmSapTkiSNocqzP6ZK0OY6icxc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\nLQwVkiSpFoYKSZJUi/8PXsEPvLb9kOAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ce8179250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_bar_graph([lsi_coherence, hdp_coherence, lda_coherence, lm_coherence, lda_lsi_coherence],\n",
    "                   ['LSI', 'HDP', 'LDA', 'LDA_Mod', 'LDA_LSI'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "df6cc31a-0b01-4700-b210-836dd510007a"
    }
   },
   "source": [
    "### Customizing the topic coherence measure\n",
    "Till now we only used the `c_v` coherence measure. There are others such as `u_mass`, `c_uci`, `c_npmi`. All of these calculate coherence in a different way. `c_v` is found to be most in line with human ratings but can be much slower than `u_mass` since it uses a sliding window over the texts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "664fa23d-ba6e-4a30-9287-e4fef1cc093e"
    }
   },
   "source": [
    "### Making your own coherence measure\n",
    "Let's modify `c_uci` to use `s_one_pre` instead of `s_one_one` segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "b3652f43-da03-4027-9170-83e1679dfa2b"
    }
   },
   "outputs": [],
   "source": [
    "from gensim.topic_coherence import (segmentation, probability_estimation,\n",
    "                                    direct_confirmation_measure, indirect_confirmation_measure,\n",
    "                                    aggregation)\n",
    "from gensim.matutils import argsort\n",
    "from collections import namedtuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "8ce2b802-f0c1-4ffc-9ce5-c2792856b5b4"
    }
   },
   "outputs": [],
   "source": [
    "make_pipeline = namedtuple('Coherence_Measure', 'seg, prob, conf, aggr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "9994e7f0-f366-40da-9c14-76e8142ee46e"
    }
   },
   "outputs": [],
   "source": [
    "measure = make_pipeline(segmentation.s_one_one,\n",
    "                        probability_estimation.p_boolean_sliding_window,\n",
    "                        direct_confirmation_measure.log_ratio_measure,\n",
    "                        aggregation.arithmetic_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "416f5820-1538-4483-a77f-5211c8179891"
    }
   },
   "source": [
    "To get topics out of the topic model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "aa9d35ae-c458-49d2-bdbe-d93d5cb3ba9c"
    }
   },
   "outputs": [],
   "source": [
    "topics = []\n",
    "for topic in lm.state.get_lambda():\n",
    "    bestn = argsort(topic, topn=10, reverse=True)\n",
    "topics.append(bestn)"
   ]
  },
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     "id": "13f246a0-918e-44c1-ae19-7af118d83585"
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   "source": [
    "__Step 1__: Segmentation"
   ]
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   "cell_type": "code",
   "execution_count": 51,
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    "collapsed": true,
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    "# Perform segmentation\n",
    "segmented_topics = measure.seg(topics)"
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   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "e4b50714-b644-4213-b3cb-bdac9fe22476"
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    "__Step 2__: Probability estimation"
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   "cell_type": "code",
   "execution_count": 52,
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    "# Since this is a window-based coherence measure we will perform window based prob estimation\n",
    "per_topic_postings, num_windows = measure.prob(texts=train_texts, segmented_topics=segmented_topics,\n",
    "                                               dictionary=dictionary, window_size=2)"
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   "source": [
    "__Step 3__: Confirmation Measure"
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    "confirmed_measures = measure.conf(segmented_topics, per_topic_postings, num_windows, normalize=False)"
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   "source": [
    "__Step 4__: Aggregation"
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   "execution_count": 54,
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      "-11.2873225334\n"
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    "print(measure.aggr(confirmed_measures))"
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   "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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@dsquareindia hi there,i was trying this code on my corpus.it was running great.but yesterday i upgraded my gensim to 2.3.0. now this code is now working.i waited for half an hour but still no output
is there any solution for this?