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devashishd12/coherence_testing.ipynb

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Testing for gensim topic coherence pipeline
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coherence_testing.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from gensim.models.coherencemodel import CoherenceModel\n",
"from gensim.models.ldamodel import LdaModel\n",
"from gensim.corpora.dictionary import Dictionary\n",
"import numpy as np\n",
"import gensim\n",
"import os\n",
"import collections\n",
"import random\n",
"from matplotlib import pyplot as plt\n",
"from scipy.stats import linregress\n",
"import pyLDAvis.gensim\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"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'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def read_corpus(fname, tokens_only=False):\n",
" with open(fname) as f:\n",
" for i, line in enumerate(f):\n",
" if tokens_only:\n",
" yield gensim.utils.simple_preprocess(line)\n",
" else:\n",
" # For training data, add tags\n",
" yield gensim.models.doc2vec.TaggedDocument(gensim.utils.simple_preprocess(line), [i])\n",
"train_corpus = list(read_corpus(lee_train_file))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"stopwords = []\n",
"for l in open('/home/devashish/stop-words/stop-words_english_1_en.txt'):\n",
" stopwords.append(l.strip())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"texts = [[word for word in document.words if word not in stopwords] for document in train_corpus]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dictionary = Dictionary(texts)\n",
"corpus = [dictionary.doc2bow(text) for text in texts]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pyLDAvis.enable_notebook()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"u_mass = []\n",
"c_v = []\n",
"l_p = []\n",
"tms = []\n",
"for iteration in range(51):\n",
" tm = LdaModel(corpus=corpus , num_topics=2, id2word=dictionary, iterations=iteration)\n",
" if iteration == 0 or iteration == 50:\n",
" tms.append(tm)\n",
" l_p.append(tm.log_perplexity(corpus))\n",
" cm1 = CoherenceModel(model=tm, corpus=corpus, coherence='u_mass')\n",
" u_mass.append(cm1.get_coherence())\n",
" cm2 = CoherenceModel(model=tm, texts=texts, coherence='c_v')\n",
" c_v.append(cm2.get_coherence())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": 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_el56791396308161543202584495388\"></div>\n",
"<script type=\"text/javascript\">\n",
"\n",
"var ldavis_el56791396308161543202584495388_data = {\"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [1, 2], \"token.table\": {\"Topic\": [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2], \"Freq\": 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\"groups\", \"hamas\", \"hamas\", \"highlands\", \"highlands\", \"humanity\", \"humanity\", \"immediately\", \"immediately\", \"impressive\", \"impressive\", \"including\", \"including\", \"india\", \"india\", \"intelligence\", \"intelligence\", \"investigating\", \"investigating\", \"involvement\", \"involvement\", \"islands\", \"islands\", \"israel\", \"israel\", \"israeli\", \"israeli\", \"issue\", \"issue\", \"january\", \"january\", \"killed\", \"killed\", \"laden\", \"laden\", \"lead\", \"lead\", \"liberation\", \"liberation\", \"line\", \"line\", \"loyalists\", \"loyalists\", \"man\", \"man\", \"match\", \"match\", \"meeting\", \"meeting\", \"militants\", \"militants\", \"minister\", \"minister\", \"murray\", \"murray\", \"nauru\", \"nauru\", \"night\", \"night\", \"northside\", \"northside\", \"pakistan\", \"pakistan\", \"palestinian\", \"palestinian\", \"passport\", \"passport\", \"people\", \"people\", \"peres\", \"peres\", \"perpetrators\", \"perpetrators\", \"police\", \"police\", \"president\", \"president\", \"pressure\", \"pressure\", \"priest\", \"priest\", \"prudential\", \"prudential\", \"qaeda\", \"qaeda\", \"qantas\", \"qantas\", \"queensland\", \"queensland\", \"reid\", \"reid\", \"released\", \"released\", \"reserve\", \"reserve\", \"romaszko\", \"romaszko\", \"royal\", \"royal\", \"scarfe\", \"scarfe\", \"security\", \"security\", \"senate\", \"senate\", \"senior\", \"senior\", \"separated\", \"separated\", \"set\", \"set\", \"shane\", \"shane\", \"shot\", \"shot\", \"singles\", \"singles\", \"skippered\", \"skippered\", \"south\", \"south\", \"spin\", \"spin\", \"standards\", \"standards\", \"states\", \"states\", \"stock\", \"stock\", \"strachan\", \"strachan\", \"suicide\", \"suicide\", \"sultan\", \"sultan\", \"surrender\", \"surrender\", \"sydney\", \"sydney\", \"taliban\", \"taliban\", \"team\", \"team\", \"terminals\", \"terminals\", \"territories\", \"territories\", \"test\", \"test\", \"time\", \"time\", \"today\", \"today\", \"told\", \"told\", \"tora\", \"tora\", \"unions\", \"unions\", \"united\", \"united\", \"warned\", \"warned\", \"west\", \"west\", \"world\", \"world\", \"yasser\", \"yasser\", \"year\", \"year\", \"zeus\", \"zeus\"]}, \"mdsDat\": {\"y\": [-0.0, -0.0], \"cluster\": [1, 1], \"Freq\": [50.041409331124079, 49.958590668875928], \"topics\": [1, 2], \"x\": [-0.0027195722491160472, 0.0027195722491160472]}, \"R\": 30, \"lambda.step\": 0.01, \"tinfo\": {\"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", 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\"emergency\", \"pakistan\", \"tora\", \"believed\", \"lead\", \"militants\", \"groups\", \"surrender\", \"early\", \"team\", \"royal\", \"downer\", \"allan\", \"night\", \"yasser\", \"general\", \"crean\", \"senior\", \"scarfe\", \"brother\", \"ahmad\", \"zeus\", \"reserve\", \"bureau\", \"territories\", \"choose\", \"eddystone\", \"priest\", \"emergency\", \"stock\", \"skippered\", \"coveted\", \"erupted\", \"strachan\", \"nauru\", \"impressive\", \"delivered\", \"involvement\", \"murray\", \"loyalists\", \"bonn\", \"advance\", \"dixon\", \"singles\", \"qantas\", \"believed\", \"lead\", \"believes\", \"final\", \"intelligence\", \"groups\", \"united\", \"killed\", \"immediately\", \"released\", \"cities\", \"pakistan\", \"afghanistan\", \"line\", \"unions\", \"night\", \"match\", \"general\", \"taliban\", \"attacks\", \"year\", \"meeting\", \"sydney\", \"people\", \"palestinian\", \"today\", \"including\", \"government\", \"suicide\", \"time\", \"south\", \"test\", \"australian\", 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"text/plain": [
"PreparedData(topic_coordinates= Freq cluster topics x y\n",
"topic \n",
"0 50.041409 1 1 -0.00272 -0.0\n",
"1 49.958591 1 2 0.00272 -0.0, topic_info= Category Freq Term Total loglift logprob\n",
"term \n",
"1767 Default 29.000000 india 29.000000 30.0000 30.0000\n",
"745 Default 71.000000 world 71.000000 29.0000 29.0000\n",
"1159 Default 128.000000 australia 128.000000 28.0000 28.0000\n",
"5540 Default 22.000000 senior 22.000000 27.0000 27.0000\n",
"6239 Default 73.000000 united 73.000000 26.0000 26.0000\n",
"5730 Default 35.000000 qantas 35.000000 25.0000 25.0000\n",
"1807 Default 39.000000 bank 39.000000 24.0000 24.0000\n",
"2450 Default 20.000000 queensland 20.000000 23.0000 23.0000\n",
"3852 Default 51.000000 killed 51.000000 22.0000 22.0000\n",
"6518 Default 14.000000 shane 14.000000 21.0000 21.0000\n",
"3753 Default 74.000000 afghanistan 74.000000 20.0000 20.0000\n",
"6351 Default 37.000000 centre 37.000000 19.0000 19.0000\n",
"812 Default 8.000000 crean 8.000000 18.0000 18.0000\n",
"667 Default 16.000000 bureau 16.000000 17.0000 17.0000\n",
"1161 Default 17.000000 emergency 17.000000 16.0000 16.0000\n",
"5587 Default 38.000000 pakistan 38.000000 15.0000 15.0000\n",
"522 Default 26.000000 tora 26.000000 14.0000 14.0000\n",
"489 Default 19.000000 believed 19.000000 13.0000 13.0000\n",
"6482 Default 21.000000 lead 21.000000 12.0000 12.0000\n",
"2839 Default 26.000000 militants 26.000000 11.0000 11.0000\n",
"5375 Default 24.000000 groups 24.000000 10.0000 10.0000\n",
"3669 Default 10.000000 surrender 10.000000 9.0000 9.0000\n",
"2377 Default 22.000000 early 22.000000 8.0000 8.0000\n",
"434 Default 30.000000 team 30.000000 7.0000 7.0000\n",
"3612 Default 24.000000 royal 24.000000 6.0000 6.0000\n",
"4097 Default 15.000000 downer 15.000000 5.0000 5.0000\n",
"127 Default 8.000000 allan 8.000000 4.0000 4.0000\n",
"1997 Default 32.000000 night 32.000000 3.0000 3.0000\n",
"4285 Default 23.000000 yasser 23.000000 2.0000 2.0000\n",
"4109 Default 43.000000 general 43.000000 1.0000 1.0000\n",
"... ... ... ... ... ... ...\n",
"621 Topic2 44.253897 israeli 82.150448 0.0754 -5.7786\n",
"5394 Topic2 66.890866 australian 128.540768 0.0408 -5.3655\n",
"6229 Topic2 42.364302 arafat 78.898616 0.0721 -5.8223\n",
"1103 Topic2 36.523127 fire 67.507892 0.0797 -5.9706\n",
"3839 Topic2 37.622846 police 69.950594 0.0738 -5.9410\n",
"921 Topic2 34.946491 states 65.070941 0.0723 -6.0148\n",
"904 Topic2 29.219469 company 53.678841 0.0858 -6.1937\n",
"4704 Topic2 58.448405 south 116.362752 0.0054 -5.5004\n",
"584 Topic2 20.994445 city 37.404938 0.1164 -6.5243\n",
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"... ... ... ...\n",
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"\n",
"[272 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[1, 2])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pyLDAvis.gensim.prepare(tms[0], corpus, dictionary)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
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-0.047500000000000001, -0.40050000000000002, -0.29110000000000003], \"Freq\": [39.0, 60.0, 30.0, 64.0, 33.0, 38.0, 124.0, 41.0, 21.0, 39.0, 52.0, 43.0, 10.0, 78.0, 12.0, 22.0, 15.0, 25.0, 78.0, 8.0, 34.0, 45.0, 23.0, 36.0, 38.0, 8.0, 34.0, 11.0, 36.0, 14.0, 5.3397164563689721, 7.6127010962542609, 3.7857320745146064, 8.2897955288340643, 8.2880155156917095, 4.4588830870397889, 9.6597592995084973, 4.456806444920475, 6.6769066739168057, 2.9642916019693151, 2.9498797749323891, 2.9359749968433606, 3.6615459330092053, 3.6434326939471258, 2.9015618086499426, 11.596559384926673, 3.6184375900256822, 2.8913522366537134, 4.3361025290721864, 7.9400583616143514, 5.7736319184567702, 5.7592564447535386, 4.3129753417192394, 5.0202780848483854, 16.467650435496086, 7.8720531005036953, 2.8601362220372635, 3.5655965214651006, 2.1388397984127328, 2.136257001663473, 9.2407477496843118, 6.3881770469212187, 42.738574300317971, 96.42426554762703, 58.55757396234717, 15.415238669911206, 48.199479281406248, 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4.019180543791105, 2.8705493677989686, 2.8613953333345887, 1.7159115990772185, 2.8505840402851703, 2.2751593326753063, 1.7040982769935056, 1.703217843730106, 4.5360654758827916, 1.7002549522986383, 1.6960245527149969, 1.6950843079616584, 3.384555283391165, 2.8161429881294544, 2.8125219340127172, 1.6849124033954956, 1.6834532693887483, 3.3635191619073734, 6.1103961470685366, 2.2326759414317134, 5.9416542357200877, 25.698799602191126, 8.4817261995956272, 4.3628374070070963, 3.2732710955659745, 2.7555158424209765, 3.2651992102058722, 13.089880672586705, 17.958503841140047, 3.7617948029317505, 18.797068205578881, 30.868061095694504, 9.4776692134750657, 19.927854402399262, 7.2379790949978178, 21.05213577786736, 30.183882194952943, 19.995017513238178, 21.051932927568796, 12.177644987361333, 24.577008302004103, 48.811987449573955, 13.141723162832825, 31.796579964416026, 11.714789684491137, 30.471918363246715, 15.822090863760939, 38.219883811465856, 19.399066293774023, 42.149949143249657, 16.537247450258736, 16.951435937464915, 22.906986755918098, 23.999729338445878, 15.519281096978593, 27.686805789188522, 23.776964389921837, 33.592611943771651, 22.466415742921342, 18.336597570645342, 16.490023905983975, 22.455314270264601, 28.562695694941617, 27.946266418751875, 20.782444306238027, 18.122642419492312, 23.297551332982248, 18.044057101632355, 18.422258210232066, 18.183069207697951], \"Total\": [39.0, 60.0, 30.0, 64.0, 33.0, 38.0, 124.0, 41.0, 21.0, 39.0, 52.0, 43.0, 10.0, 78.0, 12.0, 22.0, 15.0, 25.0, 78.0, 8.0, 34.0, 45.0, 23.0, 36.0, 38.0, 8.0, 34.0, 11.0, 36.0, 14.0, 5.9513478541257943, 8.4996583220220057, 4.2468065257205598, 9.337284134452787, 9.3370231873699616, 5.0838542390715507, 11.014848335260012, 5.0835498066546094, 7.6241075044054032, 3.3880198144456308, 3.3859070636271071, 3.3838686453742577, 4.2286010357821526, 4.2259456638743105, 3.3788237269654138, 13.513874684577395, 4.2222814215729141, 3.3773270200221139, 5.0658548054341495, 9.2860132244967932, 6.7533240179979099, 6.7512165965200257, 5.0624643967174139, 5.9045186992123515, 19.396522802913474, 9.2760437618667382, 3.3727508021516819, 4.2145350053435919, 2.5286450484531584, 2.5282664145761284, 10.953421911490583, 7.5817802023774261, 52.044027434360814, 119.72181688060928, 72.08382392057861, 18.503876069090769, 59.489871033345551, 131.79539517293145, 128.44047779015318, 45.20652800088574, 49.325016732773626, 25.962848766006864, 26.791800530035406, 17.617078710957877, 75.730336475833667, 31.764638590866177, 20.117552385467743, 26.763993348689571, 74.703589831529058, 57.384027851063713, 43.332745442373152, 53.234536526971752, 120.54892467541112, 42.38171003952219, 33.309110479824419, 23.410884100153382, 45.630769150123356, 66.092657067463804, 129.46135964582015, 30.810630702541189, 110.23682303255245, 72.280382186476416, 38.135692240098066, 66.511335231192149, 82.629337216107857, 54.359906457293583, 54.329342785946594, 124.85613792558715, 67.952464685271735, 67.727336986451348, 78.690098685892409, 78.46254664601237, 63.363059666513223, 59.224761888749569, 64.896344378061087, 4.5570030014247109, 3.0473378221805927, 3.0512319827550103, 3.0551121650767241, 3.0555914877605561, 10.709641629404203, 2.2964591222450963, 2.2971732981150099, 4.5962697052472548, 3.0651336931771187, 4.5977026708059174, 2.2990150453442744, 5.3659947658449374, 3.8329038868681153, 3.8344763784491072, 2.3008448279630351, 3.8363335559991807, 3.06997864247632, 2.3028741353239006, 2.3030253772598694, 6.1424057158087422, 2.3035343464853519, 2.3042610498591918, 2.304422566288308, 4.6098093979158445, 3.842249883359111, 3.8428719126004296, 2.306169908878636, 2.3064205607578785, 4.6134230093850501, 8.4675714940592872, 3.0772764929839256, 8.4965581923275089, 39.710734662876938, 12.386231904282006, 6.1721630518727961, 4.6289259359989767, 3.8526644908829413, 4.6303125346425436, 21.111860367024736, 30.657947284112097, 5.4102088129018435, 33.974706021380186, 60.452804787695896, 15.67595593873833, 38.106468619118189, 11.734682254683054, 41.374146528021726, 64.896344378061087, 39.825335445758547, 43.104585564396075, 22.133767458356285, 52.881468815398449, 124.85613792558715, 25.428965274927059, 78.46254664601237, 23.078479421062752, 78.690098685892409, 34.485751379779607, 110.23682303255245, 45.118921269050411, 129.46135964582015, 36.958507165478252, 38.617761535463409, 59.224761888749569, 63.363059666513223, 34.53776842327666, 82.629337216107857, 67.727336986451348, 120.54892467541112, 67.952464685271735, 47.897039771529542, 39.562225571191057, 72.280382186476416, 131.79539517293145, 128.44047779015318, 66.511335231192149, 51.394601638121415, 119.72181688060928, 51.408101123097701, 74.703589831529058, 66.092657067463804], \"logprob\": [30.0, 29.0, 28.0, 27.0, 26.0, 25.0, 24.0, 23.0, 22.0, 21.0, 20.0, 19.0, 18.0, 17.0, 16.0, 15.0, 14.0, 13.0, 12.0, 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, -8.1283999999999992, -7.7737999999999996, -8.4724000000000004, -7.6886000000000001, -7.6887999999999996, -8.3087, -7.5355999999999996, -8.3092000000000006, -7.9048999999999996, -8.7170000000000005, -8.7218, -8.7265999999999995, -8.5056999999999992, -8.5106999999999999, -8.7383000000000006, -7.3529, -8.5175999999999998, -8.7418999999999993, -8.3366000000000007, -7.7317, -8.0503, -8.0527999999999995, -8.3420000000000005, -8.1900999999999993, -7.0022000000000002, -7.7403000000000004, -8.7527000000000008, -8.5322999999999993, -9.0433000000000003, -9.0444999999999993, -7.5800000000000001, -7.9490999999999996, -6.0484999999999998, -5.2347999999999999, -5.7336, -7.0682, -5.9283000000000001, -5.1665999999999999, -5.1935000000000002, -6.2092999999999998, -6.1322999999999999, -6.7606999999999999, -6.7317, -7.1361999999999997, -5.7389000000000001, -6.5734000000000004, -7.0094000000000003, -6.7404999999999999, -5.7732000000000001, -6.0214999999999996, -6.2866, -6.0952999999999999, -5.3381999999999996, -6.3307000000000002, -6.5552999999999999, 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"text/plain": [
"PreparedData(topic_coordinates= Freq cluster topics x y\n",
"topic \n",
"1 63.193989 1 1 -0.014214 -0.0\n",
"0 36.806011 1 2 0.014214 -0.0, topic_info= Category Freq Term Total loglift logprob\n",
"term \n",
"6066 Default 39.000000 workers 39.000000 30.0000 30.0000\n",
"1725 Default 60.000000 told 60.000000 29.0000 29.0000\n",
"4747 Default 30.000000 staff 30.000000 28.0000 28.0000\n",
"1999 Default 64.000000 security 64.000000 27.0000 27.0000\n",
"271 Default 33.000000 union 33.000000 26.0000 26.0000\n",
"891 Default 38.000000 action 38.000000 25.0000 25.0000\n",
"1745 Default 124.000000 palestinian 124.000000 24.0000 24.0000\n",
"3129 Default 41.000000 cent 41.000000 23.0000 23.0000\n",
"4235 Default 21.000000 pay 21.000000 22.0000 22.0000\n",
"49 Default 39.000000 hamas 39.000000 21.0000 21.0000\n",
"3378 Default 52.000000 president 52.000000 20.0000 20.0000\n",
"5806 Default 43.000000 israel 43.000000 19.0000 19.0000\n",
"2548 Default 10.000000 banks 10.000000 18.0000 18.0000\n",
"6229 Default 78.000000 arafat 78.000000 17.0000 17.0000\n",
"1846 Default 12.000000 rates 12.000000 16.0000 16.0000\n",
"4301 Default 22.000000 statement 22.000000 15.0000 15.0000\n",
"5772 Default 15.000000 rate 15.000000 14.0000 14.0000\n",
"2589 Default 25.000000 put 25.000000 13.0000 13.0000\n",
"5607 Default 78.000000 minister 78.000000 12.0000 12.0000\n",
"2957 Default 8.000000 blake 8.000000 11.0000 11.0000\n",
"4766 Default 34.000000 national 34.000000 10.0000 10.0000\n",
"3102 Default 45.000000 officials 45.000000 9.0000 9.0000\n",
"4285 Default 23.000000 yasser 23.000000 8.0000 8.0000\n",
"584 Default 36.000000 city 36.000000 7.0000 7.0000\n",
"3552 Default 38.000000 commission 38.000000 6.0000 6.0000\n",
"6501 Default 8.000000 suharto 8.000000 5.0000 5.0000\n",
"5631 Default 34.000000 week 34.000000 4.0000 4.0000\n",
"5019 Default 11.000000 ms 11.000000 3.0000 3.0000\n",
"4281 Default 36.000000 members 36.000000 2.0000 2.0000\n",
"126 Default 14.000000 series 14.000000 1.0000 1.0000\n",
"... ... ... ... ... ... ...\n",
"3378 Topic2 24.577008 president 52.881469 0.2333 -6.0612\n",
"1745 Topic2 48.811987 palestinian 124.856138 0.0603 -5.3751\n",
"2589 Topic2 13.141723 put 25.428965 0.3394 -6.6873\n",
"6229 Topic2 31.796580 arafat 78.462547 0.0962 -5.8037\n",
"4285 Topic2 11.714790 yasser 23.078479 0.3215 -6.8022\n",
"5607 Topic2 30.471918 minister 78.690099 0.0508 -5.8462\n",
"4766 Topic2 15.822091 national 34.485751 0.2204 -6.5016\n",
"2205 Topic2 38.219884 year 110.236823 -0.0598 -5.6197\n",
"3102 Topic2 19.399066 officials 45.118921 0.1554 -6.2978\n",
"5394 Topic2 42.149949 australian 129.461360 -0.1226 -5.5218\n",
"584 Topic2 16.537247 city 36.958507 0.1953 -6.4574\n",
"3552 Topic2 16.951436 commission 38.617762 0.1761 -6.4327\n",
"411 Topic2 22.906987 time 59.224762 0.0496 -6.1316\n",
"5395 Topic2 23.999729 today 63.363060 0.0287 -6.0850\n",
"5631 Topic2 15.519281 week 34.537768 0.1995 -6.5210\n",
"621 Topic2 27.686806 israeli 82.629337 -0.0939 -5.9421\n",
"5283 Topic2 23.776964 attacks 67.727337 -0.0473 -6.0943\n",
"1591 Topic2 33.592612 government 120.548925 -0.2782 -5.7487\n",
"1103 Topic2 22.466416 fire 67.952465 -0.1073 -6.1510\n",
"5085 Topic2 18.336598 days 47.897040 0.0394 -6.3542\n",
"1807 Topic2 16.490024 bank 39.562226 0.1244 -6.4603\n",
"745 Topic2 22.455314 world 72.280382 -0.1695 -6.1515\n",
"1159 Topic2 28.562696 australia 131.795395 -0.5296 -5.9109\n",
"2028 Topic2 27.946266 people 128.440478 -0.5257 -5.9328\n",
"4667 Topic2 20.782444 day 66.511335 -0.1638 -6.2289\n",
"3852 Topic2 18.122642 killed 51.394602 -0.0429 -6.3659\n",
"4704 Topic2 23.297551 south 119.721817 -0.6373 -6.1147\n",
"2188 Topic2 18.044057 taliban 51.408101 -0.0475 -6.3702\n",
"6239 Topic2 18.422258 united 74.703590 -0.4005 -6.3495\n",
"921 Topic2 18.183069 states 66.092657 -0.2911 -6.3626\n",
"\n",
"[187 rows x 6 columns], token_table= Topic Freq Term\n",
"term \n",
"891 1 0.472361 action\n",
"891 2 0.524845 action\n",
"829 1 0.786805 adam\n",
"829 2 0.196701 adam\n",
"1566 1 0.734220 afghan\n",
"1566 2 0.262222 afghan\n",
"3753 1 0.765875 afghanistan\n",
"3753 2 0.224481 afghanistan\n",
"4551 1 0.840146 afroz\n",
"4551 2 0.168029 afroz\n",
"4450 1 0.260899 airspace\n",
"4450 2 0.782696 airspace\n",
"808 1 0.826224 al\n",
"808 2 0.172931 al\n",
"5880 1 0.328155 alcohol\n",
"5880 2 0.656311 alcohol\n",
"5940 1 0.219442 anz\n",
"5940 2 0.877770 anz\n",
"6229 1 0.599012 arafat\n",
"6229 2 0.407838 arafat\n",
"4760 1 0.778952 areas\n",
"4760 2 0.227194 areas\n",
"4819 1 0.434240 arrangements\n",
"4819 2 0.868480 arrangements\n",
"4019 1 0.327269 assist\n",
"4019 2 0.654538 assist\n",
"5283 1 0.649664 attacks\n",
"5283 2 0.354362 attacks\n",
"1159 1 0.781514 australia\n",
"1159 2 0.220038 australia\n",
"... ... ... ...\n",
"6239 2 0.240952 united\n",
"6266 1 0.434211 unjustified\n",
"6266 2 0.868423 unjustified\n",
"5496 1 0.324036 virgin\n",
"5496 2 0.648071 virgin\n",
"1019 1 0.856804 virus\n",
"1019 2 0.107101 virus\n",
"1251 1 0.888276 walker\n",
"3311 1 0.887976 warne\n",
"3311 2 0.147996 warne\n",
"5631 1 0.550122 week\n",
"5631 2 0.463261 week\n",
"4081 1 0.790674 west\n",
"4081 2 0.202737 west\n",
"5640 1 0.216033 westpac\n",
"5640 2 0.648099 westpac\n",
"591 1 0.795325 williams\n",
"591 2 0.198831 williams\n",
"6066 1 0.352550 workers\n",
"6066 2 0.654735 workers\n",
"861 1 0.435318 workloads\n",
"861 2 0.870635 workloads\n",
"745 1 0.691751 world\n",
"745 2 0.304370 world\n",
"4285 1 0.476635 yasser\n",
"4285 2 0.519965 yasser\n",
"2205 1 0.653139 year\n",
"2205 2 0.344712 year\n",
"4424 1 0.755043 yesterday\n",
"4424 2 0.235951 yesterday\n",
"\n",
"[272 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[2, 1])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pyLDAvis.gensim.prepare(tms[1], corpus, dictionary)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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2xhhjUqRWuguQaCNGjCj9vqCggIKCgrSVxRhjKpvCwkIKCwvj2oeoamJKE8vBRU4DRqjq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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7efe55348d10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = [i for i in range(51)]\n",
"plt.plot(x, u_mass, x, c_v)\n",
"plt.xlabel(\"Iterations\")\n",
"plt.ylabel(\"Coherence score\")\n",
"plt.legend((\"u_mass\", \"c_v\"), loc='best')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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8p7dmBubOorYW+vSpdC3MzLquQlb8PBGYTrqUdiIwTdIJpapYubmFY2ZWWoVc\nUhsL7BcRbwJI+jgwBbijFBUrNweOmVlpFTJKbaPGsMksLbB8h+bAMTMrrUIC40FJD0k6VdKpwF9J\nq2m2maQTJM2RVJ+zimfja2Mk1UiaJ2lUM+Uvz16fJelOSVtl+wdI+iBbBXTNSqAb4sAxMyutQgYN\nnC/pC8CIbNdvIuKudn7+bOA44LrcnZKGkPqJhgD9gSmSBucZpDAJuDAiGiRdCozJHgAvRcQ+tJID\nx8ystArpwyEi7gTuLNaHR8R8AElN19I5Frg1IuqABZJqgOHAtCblp+RsPgV8IWe7oPV5HDhmZqXV\n4iU1ScskvZ/nsUzS+yWqVz9gYc72omzfhpwGPJCzPTC7nDZV0sjmCjVy4JiZlVaLLZyIaNcqMZIm\nA31zd5Hu4xkbERPbc+yczxgLrI6IW7JdrwM7RcQ7Wd/Q3ZKGRkRtvvLjx49nyRK45ho46qgqqqqq\nilEtM7Muo7q6murq6nYdQx3h3k1JU4FzI2Jmtn0h6V7Ty7LtB4FxETEtT9lTgdOBwyJiZWuO3+S1\niAh69IC334YePYr2tczMuixJRERBXRcdaVhzbsXvBU6StKmknYFBpJtO1y0gHQGcDxyTGzaSekva\nKHu+S1b+leY+uK4OVq2CzTYrzhcxM7P1VTRwJI2WtBA4ALhP0gMAETEXmADMJQ29PrNxhJqk63OG\nUF8NbAFMbjL8+RDgeUkzs+N8OyKanWC0sf9mvaELZmZWNB3iklolSYqFC4P994dFiypdGzOzzqGz\nX1KrmNpa2LJdQyPMzKwlDhw8JNrMrBwcODhwzMzKwYGDA8fMrBwcODhwzMzKwYGDA8fMrBwcODhw\nzMzKwYGDA8fMrBwcODhwzMzKwYGDA8fMrBwcODhwzMzKwYGDA8fMrBwcODhwzMzKwYGDA8fMrBwc\nODhwzMzKwYGDA8fMrBwqveLnCZLmSKrPWcWz8bUxkmokzZM0qpnyF0t6TtKzkh6UtF0h5Rs5cMzM\nSq+iK35K2hVoAK4DzouImdn+IcAtwH5Af2AKMDiaVFbSFhFRmz0/CxgaEd+RNBS4uaXyWbno2TN4\n4w2HjplZa3W6FT8jYn5E1ABNK30scGtE1EXEAqAGGJ6nfG3OZk9SeAEc05ryjT74ADbfvM1fw8zM\nWmGTSlegGf2AJ3O2F2X71iPpJ8DXgHeBTxdaHlLYbOTeLDOzkip54EiaDPTN3QUEMDYiJrb3+BHx\nQ+CHki5amC62AAAKD0lEQVQAzgLGF36U8YzPSlVVVVFVVdXeapmZdSnV1dVUV1e36xgV7cNZUwlp\nKnBuTh/OhUBExGXZ9oPAuIiYtoFj7Aj8NSKGFVJeUgwaFNTUFP97mZl1VZ2uD6eJ3IrfC5wkaVNJ\nOwODgOnrFZAG5WyOBl4spHwjDxYwMyu9ivbhSBoNXA30Bu6TNCsijoyIuZImAHOB1cCZjSPMJF0P\n/DprDV0q6ROkwQKvAWcAbKh8Pg4cM7PS6xCX1CpJUhxxRPDAA5WuiZlZ59HZL6lVjFs4Zmal58DB\ngWNmVg4OHBw4Zmbl4MDBgWNmVg4OHBw4Zmbl4MDBgWNmVg4OHBw4Zmbl4MDBgWNmVg4OHBw4Zmbl\n4MDBgWNmVg4OHBw4Zmbl4MDBgWNmVg4OHBw4Zmbl4MDBgWNmVg4OHKBnz0rXwMys63PgAJtUdBk6\nM7PuoaKBI+kESXMk1Uvap8lrYyTVSJonaVQz5S+W9JykZyU9KGm7bP8ASR9Impk9rinH9zEzs+ZV\ndMVPSbuSloe+DjgvWzYaSUOAW4D9gP7AFGBw02WiJW0REbXZ87OAoRHxHUkDgIkRMawVddjQ6tNm\nZpZHp1vxMyLmR0QN0LTSxwK3RkRdRCwAaoDhecrX5mz2JIVXo4JOhJmZlVZH7cPpByzM2V6U7VuP\npJ9I+gdwCvDjnJcGZpfTpkoaWbqqmplZa5S8u1zSZKBv7i4ggLERMbG9x4+IHwI/lHQBcBYwHlgM\n7BQR72R9Q3dLGtqkRbTG+PHj1zyvqqqiqqqqvdUyM+tSqqurqa6ubtcxKtqHs6YS0lTg3Jw+nAuB\niIjLsu0HgXERMW0Dx9gRuD8iPtnS8Zu85j4cM7MCdbo+nCZyK34vcJKkTSXtDAwCpq9XQBqUszka\nmJft7y1po+z5Lln5V0pVcTMza1lF70CRNBq4GugN3CdpVkQcGRFzJU0A5gKrgTMbmyGSrgd+nbVW\nLpX0CdJggdeAM7JDHwJcLGlV9tq3I+Ldsn45MzNbR4e4pFZJvqRmZla4zn5JzczMujAHjpmZlYUD\nx8zMysKBY2ZmZeHAMTOzsnDgmJlZWThwzMysLBw4ZmZWFg4cMzMrCweOmZmVhQPHzMzKwoFjZmZl\n4cAxM7OycOCYmVlZOHDMzKwsKho4kk6QNEdSvaR9mrw2RlKNpHmSRrVwnHMlNUjq1ZbyZmZWepVu\n4cwGjgMeyd0paQhwIjAEOBK4RlLehX4k9QcOJ634WXB5W6u6urrSVegwfC7W8rlYy+eifSoaOBEx\nPyJqgKZhcCxwa0TURcQCoAYY3sxhfgGc347ylvH/mdbyuVjL52Itn4v2qXQLpzn9gIU524uyfeuQ\ndAywMCJmt6W8mZmVzyal/gBJk4G+ubuAAMZGxMR2HLcH8APS5TQzM+vgFBGVrgOSpgLnRsTMbPtC\nICLismz7QWBcREzLKbMHMAX4gBRi/UktmeHAaaQDXNpc+ZzjVP4EmJl1QhFRUN94yVs4Bcit+L3A\nzZJ+QboUNgiYnvvmiJgDbLemsPQqsE9EvCOpsfx/N1c+5zgeTGBmVgaVHhY9WtJC4ADgPkkPAETE\nXGACMBe4HzgzsqaYpOubDqHOBFlobai8mZlVRoe4pGZmZl1fRx2lVhaSjpD0oqT/lXRBpetTTpJu\nkLRE0vM5+7aVNEnSfEkPSdq6knUsF0n9JT0s6QVJsyX9R7a/W50PSR+VNE3Ss9l5GJft71bnIZek\njSTNzC7Td9tzIWmBpOey/zamZ/sKPhfdNnAkbQT8CvgcsDtwsqTdKlursvo96bvnuhCYEhG7Ag8D\nY8peq8qoA74XEbsDBwLfzf5b6FbnIyJWAp+OiL2BvYAjJQ2nm52HJs4mXZpv1F3PRQNQFRF7R0Tj\nPY0Fn4tuGzik0Ww1EfFaRKwGbiXdMNotRMRjwDtNdh8L3Jg9vxEYXdZKVUhEvBERs7LntcA80qjH\nbnc+IuKD7OlHSYOKgm54HmDNLCafB36bs7tbngtS/3jTvCj4XHTnwGl6c+g/8c2hfSJiCaQfYaBP\nhetTdpIGkv51/xTQt7udj+wS0rPAG8DkiJhBNzwPmcZZTHI7urvruQhgsqQZkr6V7Sv4XHSkYdHW\n8XSrESWStgDuAM6OiNo892h1+fMREQ3A3pK2Au6StDvrf+8ufx4kHQUsiYhZkqo28NYufy4yIyJi\nsaSPA5MkzacN/1105xbOImCnnO3GG0e7syWS+gJI2g54s8L1KRtJm5DC5qaIuCfb3W3PR0S8D1QD\nR9A9z8MI4BhJrwB/Bg6TdBPwRjc8F0TE4uzv/wF3k7okCv7vojsHzgxgkKQBkjYFTiLdcNqdiPVv\nuD01e/514J6mBbqw3wFzI+LKnH3d6nxI6t040iibOupwUn9WtzoPABHxg4jYKSJ2If02PBwRXwUm\n0s3OhaTNs9Y/knoCo0gz/Rf830W3vg9H0hHAlaTgvaFxKpzuQNItQBXwMWAJMI70L5fbgR1Jyz2c\nGBHvVqqO5SJpBPAo6f9EkT1+QJqdYgLd5HxI+iSp83ej7HFbRPw0W2eq25yHpiQdSpp665jueC4k\n7QzcRfr/xSbAzRFxaVvORbcOHDMzK5/ufEnNzMzKyIFjZmZl4cAxM7OycOCYmVlZOHDMzKwsHDhm\nZlYWDhyzdpK0LPs7QNLJRT72mCbbjxXz+Gbl5MAxa7/Gm9l2Bk4ppKCkjVt4yw/W+aCIkYUc36wj\nceCYFc8lwMhswa6zs5mXL88WNZsl6XRId65LelTSPcAL2b67spl4ZzfOxivpEqBHdrybsn3LGj9M\n0s+y9z8n6cScY0+VdLukeY3lstculTQnq8vlZTsrZhnPFm1WPBeSTYECkAXMuxGxfzZf3+OSJmXv\n3RvYPSL+kW1/IyLelbQZMEPSnRExRtJ3I2KfnM+I7NhfAIZFxCcl9cnKPJK9Zy9gKGmJgcclHQS8\nCIyOiN2y8luV6iSYNcctHLPSGQV8LVtfZhrQCxicvTY9J2wA/lPSLNI6PP1z3tecEaRZjImIN0kz\nO++Xc+zFkeatmgUMBN4DVkj6raTjgBXt/G5mBXPgmJWOgLOyZXn3joh/iYgp2WvL17wpTQ55GLB/\nROxFConNco7R2s9qtDLneT2wSUTUk6aUvwM4Gniw4G9j1k4OHLP2a/yxXwZsmbP/IeDMbK0dJA2W\ntHme8lsD70TESkm7AQfkvLaqsXyTz/o78KWsn+jjwMGk2a3zVzB97jYR8SDwPWBY67+eWXG4D8es\n/RpHqT0PNGSX0P4QEVdmS1bPlCTSAlX51n1/EDhD0gvAfODJnNd+Azwv6ZlsPZYAiIi7JB0APAc0\nAOdHxJuShjRTt62Ae7I+IoBz2v51zdrGyxOYmVlZ+JKamZmVhQPHzMzKwoFjZmZl4cAxM7OycOCY\nmVlZOHDMzKwsHDhmZlYWDhwzMyuL/w9Zcw5dprhiUwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7efe5425e0d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(x, l_p)\n",
"plt.xlabel(\"Iterations\")\n",
"plt.ylabel(\"log_perplexity\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-0.00336796709368\n",
"1.54813358899e-06\n"
]
}
],
"source": [
"slope_umass = linregress(x, u_mass)[0]\n",
"slope_cv = linregress(x, c_v)[0]\n",
"print slope_umass\n",
"print slope_cv"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
@devashishd12
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plugged in pyLDAvis for the 0th and 50th LDAmodel.

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