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deepnarainsingh/TopicModellingGraphLab

Created November 14, 2015 01:02
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Topic Modelling Using Graphlab on NY Times articles
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TopicModellingGraphLab
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Using GraphLab Library for Topic Modelling\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"'''Using pyLDAvis which is a library for Topic Model Visulisation\n",
"Reference taken from the below link\n",
"https://github.com/bmabey/pyLDAvis'''\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO] This non-commercial license of GraphLab Create is assigned to [email protected] and will expire on October 13, 2016. For commercial licensing options, visit https://dato.com/buy/.\n",
"\n",
"[INFO] Start server at: ipc:///tmp/graphlab_server-6158 - Server binary: /home/admin1/anaconda/lib/python2.7/site-packages/graphlab/unity_server - Server log: /tmp/graphlab_server_1447447392.log\n",
"[INFO] GraphLab Server Version: 1.6.1\n"
]
}
],
"source": [
"import graphlab as gl\n",
"import pyLDAvis \n",
"import pyLDAvis.graphlab\n",
"import pandas as pd\n",
"import ftfy"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Automatic Rendering of Visualisation\n",
"\n",
"pyLDAvis.enable_notebook()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nyArticlesDF= pd.read_pickle(\"data/articles.pkl\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Cleaning Data\n",
"\n",
"nyArticlesDF['content'] = nyArticlesDF['content'].apply(lambda x: ftfy.fix_encoding(x)\n",
" if isinstance(x, unicode)\n",
" else \"Warning: not Unicode\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 row isn't unicode\n"
]
}
],
"source": [
"#There is one row which is not unicoded\n",
"print(\"{} row isn't unicode\".format(sum(nyArticlesDF['content'] == \"Warning: not Unicode\")))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Dropping the row which is not unicoded\n",
"\n",
"nyArticlesDF = nyArticlesDF[nyArticlesDF['content'] != \"Warning: not Unicode\"]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 row isn't unicode\n"
]
}
],
"source": [
"print(\"{} row isn't unicode\".format(sum(nyArticlesDF['content'] == \"Warning: not Unicode\")))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Passing the Data Frame from pandas as input to GraphLab SFrame\n",
"\n",
"nyArticlesSFrame= gl.SFrame(nyArticlesDF)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Canvas is accessible via web browser at the URL: http://localhost:40516/index.html\n",
"Opening Canvas in default web browser.\n"
]
}
],
"source": [
"nyArticlesSFrame.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"'''Constructing Bag-of-Words Classifier\n",
"One of the common techniques to perform document classification (and reviews classification) \n",
"is using Bag-of-Words model, in which the frequency of each word in the document is used \n",
"as a feature for training a classifier. GraphLab's text analytics toolkit makes it easy \n",
"to calculate the frequency of each word in each review. Namely, by using the count_ngrams \n",
"function with n=1, we can calculate the frequency of each word in each review. '''"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nyArticlesSFrame['features'] = gl.text_analytics.count_ngrams(nyArticlesSFrame ['content'],1)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[WARNING] Column selection for SFrame.show is deprecated. To show only certain columns, use the sf[['column1', 'column2']] syntax or construct a new SFrame with the desired columns.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Canvas is accessible via web browser at the URL: http://localhost:40516/index.html\n",
"Opening Canvas in default web browser.\n"
]
}
],
"source": [
"nyArticlesSFrame.show(['content','features'])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PROGRESS: Learning a topic model\n",
"PROGRESS: Number of documents 1404\n",
"PROGRESS: Vocabulary size 39457\n",
"PROGRESS: Running collapsed Gibbs sampling\n",
"PROGRESS: +-----------+---------------+----------------+-----------------+\n",
"PROGRESS: | Iteration | Elapsed Time | Tokens/Second | Est. Perplexity |\n",
"PROGRESS: +-----------+---------------+----------------+-----------------+\n",
"PROGRESS: | 10 | 932.031ms | 4.72706e+06 | 0 |\n",
"PROGRESS: | 20 | 1.81s | 4.65163e+06 | 0 |\n",
"PROGRESS: | 30 | 2.66s | 4.37996e+06 | 0 |\n",
"PROGRESS: | 40 | 3.49s | 4.55774e+06 | 0 |\n",
"PROGRESS: | 50 | 4.33s | 4.27139e+06 | 0 |\n",
"PROGRESS: +-----------+---------------+----------------+-----------------+\n"
]
}
],
"source": [
"#Creating Topic Model\n",
"#gl.topic_model.create?\n",
"topicModel=gl.topic_model.create(nyArticlesSFrame['features'],num_topics=10,num_iterations=50)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#topicModel.get_topics(topic_ids=[0,1,2,3,4,5,6,7,8,9],num_words=5).print_rows(50,3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visualsation Of Topic Model\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 14,
"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_el61271397022065983527425220677\"></div>\n",
"<script type=\"text/javascript\">\n",
"\n",
"var ldavis_el61271397022065983527425220677_data = {\"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [5, 10, 8, 9, 4, 6, 1, 2, 7, 3], \"token.table\": {\"Topic\": [7, 1, 3, 10, 9, 8, 2, 7, 1, 6, 7, 8, 3, 8, 6, 6, 4, 2, 4, 4, 8, 9, 8, 5, 7, 2, 7, 3, 7, 6, 2, 3, 6, 4, 1, 7, 6, 5, 1, 2, 5, 7, 2, 9, 9, 8, 10, 1, 6, 7, 10, 8, 2, 3, 1, 9, 2, 5, 10, 10, 10, 5, 6, 6, 4, 5, 3, 5, 4, 10, 3, 8, 2, 5, 6, 10, 1, 3, 5, 6, 7, 8, 7, 1, 3, 4, 10, 10, 5, 2, 3, 1, 7, 9, 10, 10, 10, 9, 9, 10, 3, 1, 6, 7, 8, 8, 9, 10, 10, 1, 1, 4, 8, 10, 7, 3, 5, 7, 8, 4, 1, 5, 8, 9, 1, 6, 7, 10, 2, 7, 8, 10, 8, 1, 5, 5, 10, 4, 7, 2, 5, 10, 7, 9, 10, 3, 9, 4, 2, 7, 10, 1, 5, 2, 8, 10, 4, 3, 5, 5, 3, 3, 8, 4, 1, 9, 3, 5, 8, 3, 1, 8, 1, 4, 8, 5, 6, 4, 9, 1, 6, 5, 2, 2, 3, 1, 3, 1, 8, 4, 8, 5, 8, 9, 10, 8, 6, 7, 5, 7, 1, 9, 8, 5, 9, 7, 10, 10, 5, 3, 5, 2, 2, 5, 4, 3, 4, 3, 1, 9, 10, 2, 7, 6, 1, 6, 8, 4, 7, 8, 7, 10, 8, 1, 2, 4, 8, 8, 10, 9, 3, 5, 8, 4, 1, 6, 2, 10, 3, 7, 1, 3, 1, 1, 8, 1, 9, 2, 3, 4, 7, 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"\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_el61271397022065983527425220677\", ldavis_el61271397022065983527425220677_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_el61271397022065983527425220677\", ldavis_el61271397022065983527425220677_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_el61271397022065983527425220677\", ldavis_el61271397022065983527425220677_data);\n",
" })\n",
" });\n",
"}\n",
"</script>"
],
"text/plain": [
"PreparedData(topic_coordinates= Freq cluster topics x y\n",
"topic \n",
"4 21.102693 1 1 0.313777 -0.086503\n",
"9 9.623792 1 2 -0.014117 0.091459\n",
"7 9.478882 1 3 -0.035058 0.071711\n",
"8 9.222720 1 4 0.045782 -0.233297\n",
"3 9.091752 1 5 -0.019211 0.192677\n",
"5 8.889752 1 6 -0.167274 0.081736\n",
"0 8.532668 1 7 0.202893 0.162013\n",
"1 8.498027 1 8 -0.079208 -0.208469\n",
"6 7.956741 1 9 -0.113860 0.004066\n",
"2 7.602974 1 10 -0.133724 -0.075392, topic_info= Category Freq Term Total loglift logprob\n",
"34721 Default 11966.000000 said 11966 30.0000 30.0000\n",
"17196 Default 8298.000000 mr 8298 29.0000 29.0000\n",
"12525 Default 11443.000000 the 11443 28.0000 28.0000\n",
"7177 Default 4961.000000 would 4961 27.0000 27.0000\n",
"17066 Default 6991.000000 ” 6991 26.0000 26.0000\n",
"3142 Default 5850.000000 year 5850 25.0000 25.0000\n",
"9942 Default 4683.000000 new 4683 24.0000 24.0000\n",
"23771 Default 2845.000000 game 2845 23.0000 23.0000\n",
"14299 Default 4917.000000 one 4917 22.0000 22.0000\n",
"33220 Default 2431.000000 a 2431 21.0000 21.0000\n",
"34099 Default 3182.000000 state 3182 20.0000 20.0000\n",
"37838 Default 2345.000000 season 2345 19.0000 19.0000\n",
"10257 Default 2162.000000 team 2162 18.0000 18.0000\n",
"5570 Default 2964.000000 first 2964 17.0000 17.0000\n",
"19109 Default 2115.000000 united 2115 16.0000 16.0000\n",
"21911 Default 3035.000000 government 3035 15.0000 15.0000\n",
"10110 Default 2733.000000 people 2733 14.0000 14.0000\n",
"1533 Default 2911.000000 last 2911 13.0000 13.0000\n",
"25850 Default 3454.000000 two 3454 12.0000 12.0000\n",
"38252 Default 1789.000000 and 1789 11.0000 11.0000\n",
"14577 Default 2615.000000 he 2615 10.0000 10.0000\n",
"30085 Default 3359.000000 time 3359 9.0000 9.0000\n",
"15042 Default 3391.000000 in 3391 8.0000 8.0000\n",
"24648 Default 1983.000000 president 1983 7.0000 7.0000\n",
"35866 Default 1588.000000 house 1588 6.0000 6.0000\n",
"17015 Default 3775.000000 but 3775 5.0000 5.0000\n",
"5506 Default 1563.000000 group 1563 4.0000 4.0000\n",
"6985 Default 3244.000000 like 3244 3.0000 3.0000\n",
"32035 Default 1892.000000 work 1892 2.0000 2.0000\n",
"12040 Default 1580.000000 official 1580 1.0000 1.0000\n",
"... ... ... ... ... ... ...\n",
"23107 Topic10 127.105731 commander 128 2.5440 -6.5594\n",
"5878 Topic10 733.568058 woman 767 2.5007 -4.8122\n",
"16776 Topic10 201.109112 supporter 204 2.5339 -6.1035\n",
"12040 Topic10 1357.029916 official 1580 2.3919 -4.1984\n",
"19938 Topic10 1339.040998 country 1586 2.3748 -4.2117\n",
"15225 Topic10 312.950348 died 340 2.4640 -5.6625\n",
"36931 Topic10 209.065374 shooting 219 2.5017 -6.0646\n",
"10033 Topic10 251.048891 visit 271 2.4709 -5.8824\n",
"34099 Topic10 1937.797451 state 3182 2.0515 -3.8386\n",
"21224 Topic10 391.660217 south 466 2.3731 -5.4382\n",
"23364 Topic10 611.515943 family 855 2.2123 -4.9921\n",
"33043 Topic10 464.220558 area 604 2.2819 -5.2700\n",
"10110 Topic10 1436.273098 people 2733 1.9046 -4.1377\n",
"17195 Topic10 783.970265 ms 1651 1.8088 -4.7375\n",
"14332 Topic10 411.107023 she 618 2.1428 -5.3861\n",
"15972 Topic10 264.854067 town 322 2.3525 -5.8283\n",
"11247 Topic10 222.144987 fire 241 2.4661 -6.0046\n",
"14299 Topic10 1104.969638 one 4917 1.0568 -4.3982\n",
"26010 Topic10 404.582040 least 744 1.9378 -5.4056\n",
"959 Topic10 448.192778 former 993 1.7520 -5.3027\n",
"19402 Topic10 450.135465 member 1207 1.5568 -5.3027\n",
"22305 Topic10 277.291422 country’s 379 2.2337 -5.7842\n",
"24507 Topic10 326.913430 whose 614 1.9196 -5.6159\n",
"21861 Topic10 328.474446 site 630 1.8969 -5.6128\n",
"39187 Topic10 282.249803 view 400 2.1976 -5.7664\n",
"5393 Topic10 344.292904 international 814 1.6851 -5.5684\n",
"25816 Topic10 337.898350 public 988 1.4768 -5.5830\n",
"36494 Topic10 361.168406 day 2124 0.7630 -5.5314\n",
"3104 Topic10 298.316722 still 1500 0.9341 -5.7081\n",
"16801 Topic10 288.747241 national 1156 1.1503 -5.7524\n",
"\n",
"[1039 rows x 6 columns], token_table= Topic Freq Term\n",
"term \n",
"5351 7 0.998731 0\n",
"3966 1 0.451394 000\n",
"3966 3 0.412208 000\n",
"3966 10 0.135644 000\n",
"21929 9 0.500000 0600\n",
"5316 8 0.500000 088\n",
"27752 2 0.150929 1\n",
"27752 7 0.848297 1\n",
"25430 1 0.233058 10\n",
"25430 6 0.085950 10\n",
"25430 7 0.678512 10\n",
"25430 8 0.001653 10\n",
"34773 3 0.500000 169\n",
"32681 8 0.500000 1835\n",
"28290 6 0.500000 1840\n",
"28289 6 0.500000 1841\n",
"18462 4 0.500000 1909\n",
"10470 2 0.301181 2\n",
"10470 4 0.697835 2\n",
"21612 4 0.181132 2013\n",
"21612 8 0.035849 2013\n",
"21612 9 0.781132 2013\n",
"30567 8 0.992593 212\n",
"5315 5 0.500000 2500\n",
"25922 7 0.995146 27\n",
"32866 2 0.059649 3\n",
"32866 7 0.939181 3\n",
"22517 3 0.500000 323\n",
"15719 7 0.998384 4\n",
"28084 6 0.500000 46th\n",
"... ... ... ...\n",
"12416 9 0.500000 “gloria\n",
"31776 8 0.500000 “gone\n",
"22397 3 0.500000 “grab\n",
"12420 9 0.500000 “horse\n",
"38758 2 0.846367 “i\n",
"38758 7 0.152941 “i\n",
"3484 2 0.500000 “illegal\n",
"35240 4 0.500000 “m\n",
"10553 5 0.500000 “mccain\n",
"29887 4 0.500000 “orchestral\n",
"8664 7 0.500000 “phineas\n",
"4996 5 0.500000 “possible\n",
"28129 6 0.500000 “puzzle\n",
"8861 10 0.500000 “republic\n",
"34796 3 0.500000 “smooth\n",
"8832 6 0.500000 “solomon\n",
"30253 7 0.500000 “song\n",
"20448 4 0.500000 “teams\n",
"10535 1 0.915501 “the\n",
"10535 4 0.082885 “the\n",
"10535 10 0.001076 “the\n",
"7898 4 0.500000 “thrones”\n",
"13820 3 0.500000 “title\n",
"32634 8 0.500000 “tragic”\n",
"28070 1 0.500000 “unclear”\n",
"12167 5 0.500000 “usually\n",
"18070 8 0.500000 “whales”\n",
"17066 1 0.929624 ”\n",
"17066 5 0.070233 ”\n",
"5015 6 0.500000 €54\n",
"\n",
"[1419 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[5, 10, 8, 9, 4, 6, 1, 2, 7, 3])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pyLDAvis.graphlab.prepare(topicModel, nyArticlesSFrame['features'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
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"nbformat": 4,
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