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imranshaikmuma/graphlab demo

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graphlab demo
{
"cells": [
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"cell_type": "code",
"execution_count": 13,
"metadata": {
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},
"outputs": [],
"source": [
"import graphlab as gl\n",
"import pyLDAvis\n",
"import pyLDAvis.graphlab"
]
},
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"\n",
"pyLDAvis.enable_notebook()"
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"source": [
"docs = gl.SArray('http://s3.amazonaws.com/GraphLab-Datasets/nytimes')"
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"cell_type": "code",
"execution_count": 16,
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"<function graphlab.toolkits.topic_model.topic_model.create>"
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"source": [
"\n",
"gl.topic_model.create"
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"text/html": [
"<pre>Learning a topic model</pre>"
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"Learning a topic model"
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"<pre> Number of documents 10000</pre>"
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" Number of documents 10000"
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"text/html": [
"<pre> Vocabulary size 63958</pre>"
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" Vocabulary size 63958"
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"text/html": [
"<pre> Running collapsed Gibbs sampling</pre>"
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" Running collapsed Gibbs sampling"
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"+-----------+---------------+----------------+-----------------+"
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"| Iteration | Elapsed Time | Tokens/Second | Est. Perplexity |"
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"text/html": [
"<pre>| 1590 | 7m 18s | 8.14136e+006 | 0 |</pre>"
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"<pre>| 1610 | 7m 23s | 8.20291e+006 | 0 |</pre>"
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"<pre>| 1660 | 7m 38s | 8.55885e+006 | 0 |</pre>"
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"<pre>| 1670 | 7m 40s | 8.36095e+006 | 0 |</pre>"
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"<pre>| 1680 | 7m 43s | 8.00617e+006 | 0 |</pre>"
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"<pre>| 1710 | 7m 51s | 8.85572e+006 | 0 |</pre>"
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"<pre>| 1720 | 7m 53s | 8.5085e+006 | 0 |</pre>"
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"<pre>| 1730 | 7m 56s | 8.4096e+006 | 0 |</pre>"
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"<pre>| 1740 | 7m 58s | 8.96558e+006 | 0 |</pre>"
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"text/html": [
"<pre>| 1750 | 8m 1s | 8.32885e+006 | 0 |</pre>"
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"text/html": [
"<pre>| 1760 | 8m 4s | 8.36095e+006 | 0 |</pre>"
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"text/html": [
"<pre>| 1770 | 8m 6s | 7.9621e+006 | 0 |</pre>"
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"data": {
"text/html": [
"<pre>| 1780 | 8m 9s | 7.53357e+006 | 0 |</pre>"
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"data": {
"text/html": [
"<pre>| 1790 | 8m 12s | 7.93295e+006 | 0 |</pre>"
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"| 1790 | 8m 12s | 7.93295e+006 | 0 |"
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"metadata": {},
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"data": {
"text/html": [
"<pre>| 1800 | 8m 15s | 5.97701e+006 | 0 |</pre>"
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"| 1800 | 8m 15s | 5.97701e+006 | 0 |"
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},
"metadata": {},
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"data": {
"text/html": [
"<pre>| 1810 | 8m 18s | 8.172e+006 | 0 |</pre>"
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"| 1810 | 8m 18s | 8.172e+006 | 0 |"
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"data": {
"text/html": [
"<pre>| 1820 | 8m 21s | 8.49186e+006 | 0 |</pre>"
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"| 1820 | 8m 21s | 8.49186e+006 | 0 |"
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},
"metadata": {},
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},
{
"data": {
"text/html": [
"<pre>| 1830 | 8m 24s | 8.69604e+006 | 0 |</pre>"
],
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"| 1830 | 8m 24s | 8.69604e+006 | 0 |"
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},
"metadata": {},
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},
{
"data": {
"text/html": [
"<pre>| 1840 | 8m 27s | 8.32888e+006 | 0 |</pre>"
],
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"| 1840 | 8m 27s | 8.32888e+006 | 0 |"
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},
"metadata": {},
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{
"data": {
"text/html": [
"<pre>| 1850 | 8m 29s | 7.54663e+006 | 0 |</pre>"
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"| 1850 | 8m 29s | 7.54663e+006 | 0 |"
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},
"metadata": {},
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{
"data": {
"text/html": [
"<pre>| 1860 | 8m 32s | 7.76265e+006 | 0 |</pre>"
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"| 1860 | 8m 32s | 7.76265e+006 | 0 |"
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},
"metadata": {},
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{
"data": {
"text/html": [
"<pre>| 1870 | 8m 35s | 8.03583e+006 | 0 |</pre>"
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"| 1870 | 8m 35s | 8.03583e+006 | 0 |"
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},
"metadata": {},
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{
"data": {
"text/html": [
"<pre>| 1880 | 8m 38s | 7.96207e+006 | 0 |</pre>"
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"| 1880 | 8m 38s | 7.96207e+006 | 0 |"
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"metadata": {},
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{
"data": {
"text/html": [
"<pre>| 1890 | 8m 41s | 8.34485e+006 | 0 |</pre>"
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"| 1890 | 8m 41s | 8.34485e+006 | 0 |"
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"metadata": {},
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"data": {
"text/html": [
"<pre>| 1900 | 8m 44s | 7.07886e+006 | 0 |</pre>"
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"| 1900 | 8m 44s | 7.07886e+006 | 0 |"
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"metadata": {},
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{
"data": {
"text/html": [
"<pre>| 1910 | 8m 47s | 7.52052e+006 | 0 |</pre>"
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"| 1910 | 8m 47s | 7.52052e+006 | 0 |"
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},
"metadata": {},
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{
"data": {
"text/html": [
"<pre>| 1920 | 8m 50s | 6.19015e+006 | 0 |</pre>"
],
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"| 1920 | 8m 50s | 6.19015e+006 | 0 |"
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"metadata": {},
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{
"data": {
"text/html": [
"<pre>| 1930 | 8m 53s | 6.84436e+006 | 0 |</pre>"
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"| 1930 | 8m 53s | 6.84436e+006 | 0 |"
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},
"metadata": {},
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"data": {
"text/html": [
"<pre>| 1940 | 8m 57s | 5.11112e+006 | 0 |</pre>"
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"| 1940 | 8m 57s | 5.11112e+006 | 0 |"
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"metadata": {},
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"data": {
"text/html": [
"<pre>| 1950 | 9m 0s | 7.39238e+006 | 0 |</pre>"
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"| 1950 | 9m 0s | 7.39238e+006 | 0 |"
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"metadata": {},
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"data": {
"text/html": [
"<pre>| 1960 | 9m 3s | 7.8754e+006 | 0 |</pre>"
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"| 1960 | 9m 3s | 7.8754e+006 | 0 |"
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"metadata": {},
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"data": {
"text/html": [
"<pre>| 1970 | 9m 6s | 5.89584e+006 | 0 |</pre>"
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"| 1970 | 9m 6s | 5.89584e+006 | 0 |"
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"metadata": {},
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"data": {
"text/html": [
"<pre>| 1980 | 9m 9s | 8.34485e+006 | 0 |</pre>"
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"| 1980 | 9m 9s | 8.34485e+006 | 0 |"
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"metadata": {},
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"data": {
"text/html": [
"<pre>| 1990 | 9m 12s | 8.49547e+006 | 0 |</pre>"
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"metadata": {},
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"data": {
"text/html": [
"<pre>| 2000 | 9m 14s | 8.71352e+006 | 0 |</pre>"
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"text/html": [
"<pre>| 2010 | 9m 17s | 7.13706e+006 | 0 |</pre>"
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"text/html": [
"<pre>| 2020 | 9m 20s | 7.99138e+006 | 0 |</pre>"
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"data": {
"text/html": [
"<pre>| 2030 | 9m 22s | 8.67873e+006 | 0 |</pre>"
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"text/html": [
"<pre>| 2040 | 9m 25s | 8.80187e+006 | 0 |</pre>"
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"text/html": [
"<pre>| 2050 | 9m 27s | 8.81982e+006 | 0 |</pre>"
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"| 2050 | 9m 27s | 8.81982e+006 | 0 |"
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"text/html": [
"<pre>| 2060 | 9m 30s | 8.50853e+006 | 0 |</pre>"
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"text/html": [
"<pre>| 2070 | 9m 33s | 8.69607e+006 | 0 |</pre>"
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"text/html": [
"<pre>| 2080 | 9m 35s | 5.63549e+006 | 0 |</pre>"
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"text/html": [
"<pre>| 2090 | 9m 39s | 6.56481e+006 | 0 |</pre>"
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"text/html": [
"<pre>| 2100 | 9m 42s | 7.30528e+006 | 0 |</pre>"
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"<pre>| 2110 | 9m 45s | 8.09453e+006 | 0 |</pre>"
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"text/html": [
"<pre>| 2120 | 9m 48s | 8.12606e+006 | 0 |</pre>"
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"text/html": [
"<pre>| 2130 | 9m 50s | 7.12534e+006 | 0 |</pre>"
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"text/html": [
"<pre>| 2140 | 9m 53s | 7.41769e+006 | 0 |</pre>"
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"text/html": [
"<pre>| 2150 | 9m 56s | 7.90407e+006 | 0 |</pre>"
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"<pre>| 2160 | 9m 59s | 8.02096e+006 | 0 |</pre>"
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"text/html": [
"<pre>| 2170 | 10m 2s | 7.58621e+006 | 0 |</pre>"
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"text/html": [
"<pre>| 2180 | 10m 5s | 7.81851e+006 | 0 |</pre>"
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"text/html": [
"<pre>| 2190 | 10m 7s | 8.15668e+006 | 0 |</pre>"
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"text/html": [
"<pre>| 2200 | 10m 10s | 8.0482e+006 | 0 |</pre>"
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"text/html": [
"<pre>| 2210 | 10m 13s | 8.18741e+006 | 0 |</pre>"
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"text/html": [
"<pre>| 2220 | 10m 16s | 8.05067e+006 | 0 |</pre>"
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"metadata": {},
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"text/html": [
"<pre>| 2230 | 10m 18s | 7.50749e+006 | 0 |</pre>"
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"text/html": [
"<pre>| 2240 | 10m 22s | 7.03297e+006 | 0 |</pre>"
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"outputs": [
{
"data": {
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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_el86446843376966669924637\", ldavis_el86446843376966669924637_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_el86446843376966669924637\", ldavis_el86446843376966669924637_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_el86446843376966669924637\", ldavis_el86446843376966669924637_data);\n",
" })\n",
" });\n",
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"</script>"
],
"text/plain": [
"PreparedData(topic_coordinates= Freq cluster topics x y\n",
"topic \n",
"6 8.341027 1 1 0.090689 -0.110657\n",
"14 8.266867 1 2 0.156342 0.073970\n",
"4 7.688328 1 3 -0.133869 0.247192\n",
"9 7.417673 1 4 -0.079098 0.122206\n",
"5 7.254663 1 5 0.210232 -0.056665\n",
"8 7.037524 1 6 0.157248 0.016461\n",
"10 7.016594 1 7 -0.156586 0.022444\n",
"0 6.716629 1 8 -0.197660 -0.066269\n",
"12 6.460550 1 9 0.181413 0.060980\n",
"13 6.217595 1 10 0.044114 -0.012954\n",
"3 5.878395 1 11 -0.053533 -0.172749\n",
"7 5.802476 1 12 -0.032809 0.211770\n",
"11 5.678666 1 13 -0.003914 -0.052707\n",
"2 5.565540 1 14 0.023550 -0.132213\n",
"1 4.657473 1 15 -0.206119 -0.150810, topic_info= Category Freq Term Total loglift logprob\n",
"term \n",
"16975 Default 6907.000000 team 6907.000000 30.0000 30.0000\n",
"38603 Default 6128.000000 game 6128.000000 29.0000 29.0000\n",
"55971 Default 5535.000000 campaign 5535.000000 28.0000 28.0000\n",
"2124 Default 6004.000000 company 6004.000000 27.0000 27.0000\n",
"36803 Default 4454.000000 zzz_al_gore 4454.000000 26.0000 26.0000\n",
"63714 Default 8130.000000 percent 8130.000000 25.0000 25.0000\n",
"48402 Default 5185.000000 season 5185.000000 24.0000 24.0000\n",
"18415 Default 4951.000000 play 4951.000000 23.0000 23.0000\n",
"17274 Default 6992.000000 million 6992.000000 22.0000 22.0000\n",
"55419 Default 2894.000000 computer 2894.000000 21.0000 21.0000\n",
"36018 Default 3868.000000 player 3868.000000 20.0000 20.0000\n",
"53029 Default 3512.000000 market 3512.000000 19.0000 19.0000\n",
"23905 Default 3555.000000 companies 3555.000000 18.0000 18.0000\n",
"3691 Default 2818.000000 web 2818.000000 17.0000 17.0000\n",
"20486 Default 3410.000000 com 3410.000000 16.0000 16.0000\n",
"7636 Default 4056.000000 school 4056.000000 15.0000 15.0000\n",
"44901 Default 3341.000000 coach 3341.000000 14.0000 14.0000\n",
"41339 Default 4977.000000 show 4977.000000 13.0000 13.0000\n",
"22043 Default 2460.000000 site 2460.000000 12.0000 12.0000\n",
"13484 Default 3143.000000 case 3143.000000 11.0000 11.0000\n",
"41811 Default 2540.000000 film 2540.000000 10.0000 10.0000\n",
"35640 Default 4203.000000 government 4203.000000 9.0000 9.0000\n",
"52110 Default 3060.000000 law 3060.000000 8.0000 8.0000\n",
"42644 Default 2665.000000 zzz_bill_bradley 2665.000000 7.0000 7.0000\n",
"37935 Default 3262.000000 family 3262.000000 6.0000 6.0000\n",
"10411 Default 5304.000000 point 5304.000000 5.0000 5.0000\n",
"46130 Default 2457.000000 court 2457.000000 4.0000 4.0000\n",
"31870 Default 2491.000000 zzz_george_bush 2491.000000 3.0000 3.0000\n",
"30002 Default 3383.000000 program 3383.000000 2.0000 2.0000\n",
"19811 Default 4840.000000 official 4840.000000 1.0000 1.0000\n",
"... ... ... ... ... ... ...\n",
"49442 Topic15 267.054914 salt 270.368126 3.0544 -6.2884\n",
"19534 Topic15 409.359554 taste 419.446414 3.0424 -5.8613\n",
"22154 Topic15 1361.865800 food 1470.868848 2.9897 -4.6593\n",
"54068 Topic15 326.261954 egg 332.556044 3.0476 -6.0882\n",
"42209 Topic15 448.830915 fish 464.010755 3.0334 -5.7692\n",
"50201 Topic15 385.468994 bowl 398.167434 3.0343 -5.9214\n",
"10394 Topic15 737.595076 restaurant 858.938010 2.9144 -5.2725\n",
"61937 Topic15 468.566595 fat 513.376544 2.9754 -5.7262\n",
"42511 Topic15 294.061634 meal 304.259488 3.0326 -6.1921\n",
"58173 Topic15 831.079877 oil 1081.844808 2.8030 -5.1531\n",
"5154 Topic15 514.270275 serving 694.475710 2.7663 -5.6331\n",
"28846 Topic15 376.120514 medium 440.497461 2.9087 -5.9460\n",
"40147 Topic15 328.339394 meat 360.310723 2.9738 -6.0818\n",
"27846 Topic15 1116.727878 minutes 2335.969379 2.3287 -4.8577\n",
"60961 Topic15 820.692677 water 1557.896045 2.4258 -5.1657\n",
"43704 Topic15 684.620356 add 1300.894100 2.4248 -5.3470\n",
"34852 Topic15 442.598595 fresh 620.305640 2.7291 -5.7832\n",
"48149 Topic15 505.960515 hot 850.748250 2.5470 -5.6494\n",
"28338 Topic15 510.115395 serve 890.918840 2.5091 -5.6412\n",
"57313 Topic15 413.514434 green 642.601867 2.6259 -5.8512\n",
"33928 Topic15 645.148996 large 1971.770297 1.9495 -5.4064\n",
"50636 Topic15 460.256835 pound 868.655323 2.4315 -5.7441\n",
"11885 Topic15 375.081794 leaves 556.510180 2.6722 -5.9487\n",
"2024 Topic15 526.734915 small 2344.730549 1.5735 -5.6092\n",
"3069 Topic15 501.805635 makes 1926.675292 1.7214 -5.6577\n",
"4651 Topic15 510.115395 hand 2675.195456 1.4096 -5.6412\n",
"35913 Topic15 398.972354 red 781.874273 2.3939 -5.8870\n",
"61292 Topic15 422.862914 white 1617.388206 1.7252 -5.8288\n",
"9973 Topic15 439.482435 hour 3000.233667 1.1458 -5.7903\n",
"19107 Topic15 412.475714 high 4317.624250 0.7184 -5.8537\n",
"\n",
"[1059 rows x 6 columns], token_table= Topic Freq Term\n",
"term \n",
"1163 2 0.997054 abortion\n",
"48559 6 0.008363 accident\n",
"48559 7 0.019514 accident\n",
"48559 10 0.002788 accident\n",
"48559 13 0.947845 accident\n",
"48559 14 0.019514 accident\n",
"48371 1 0.993870 acquisition\n",
"16882 7 0.998211 actor\n",
"45675 7 0.996927 actress\n",
"43704 1 0.063034 add\n",
"43704 5 0.042279 add\n",
"43704 6 0.006150 add\n",
"43704 7 0.210624 add\n",
"43704 8 0.001537 add\n",
"43704 11 0.099931 add\n",
"43704 12 0.009224 add\n",
"43704 14 0.039973 add\n",
"43704 15 0.526561 add\n",
"57102 6 0.212631 african\n",
"57102 10 0.783236 african\n",
"50926 4 0.515315 age\n",
"50926 7 0.072197 age\n",
"50926 8 0.033118 age\n",
"50926 10 0.080808 age\n",
"50926 11 0.144394 age\n",
"50926 12 0.014572 age\n",
"50926 13 0.002649 age\n",
"50926 14 0.131809 age\n",
"50926 15 0.004637 age\n",
"57055 2 0.000774 agent\n",
"... ... ... ...\n",
"13088 5 0.307869 zzz_union\n",
"13088 6 0.689039 zzz_union\n",
"13088 9 0.001222 zzz_union\n",
"2974 13 0.979932 zzz_united\n",
"32145 6 0.982950 zzz_united_nation\n",
"32145 10 0.013019 zzz_united_nation\n",
"24573 1 0.085576 zzz_united_states\n",
"24573 5 0.002728 zzz_united_states\n",
"24573 6 0.543121 zzz_united_states\n",
"24573 9 0.216498 zzz_united_states\n",
"24573 10 0.031708 zzz_united_states\n",
"24573 11 0.000341 zzz_united_states\n",
"24573 13 0.097850 zzz_united_states\n",
"24573 14 0.021820 zzz_united_states\n",
"28353 3 0.994508 zzz_usc\n",
"22517 3 0.994132 zzz_virginia_tech\n",
"17257 4 0.982395 zzz_virtue\n",
"53972 6 0.997883 zzz_vladimir_putin\n",
"37329 1 0.996003 zzz_wall_street\n",
"56072 1 0.989109 zzz_warner_lambert\n",
"43716 6 0.993420 zzz_will_putin\n",
"47766 4 0.963293 zzz_wolf\n",
"27110 12 0.986875 zzz_world_series\n",
"12310 4 0.017429 zzz_world_wide\n",
"12310 6 0.017429 zzz_world_wide\n",
"12310 11 0.958597 zzz_world_wide\n",
"27039 4 0.930978 zzz_yale\n",
"27039 12 0.042317 zzz_yale\n",
"27039 14 0.010579 zzz_yale\n",
"8910 5 0.982019 zzz_yasser_arafat\n",
"\n",
"[3118 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[7, 15, 5, 10, 6, 9, 11, 1, 13, 14, 4, 8, 12, 3, 2])"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pyLDAvis.graphlab.prepare(topic_model, docs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print topic_model"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Class : TopicModel\n",
"\n",
"Schema\n",
"------\n",
"Vocabulary Size : 63958\n",
"\n",
"Settings\n",
"--------\n",
"Number of Topics : 15\n",
"alpha : 3.3333\n",
"beta : 0.1\n",
"Iterations : 3000\n",
"Training time : 838.4709\n",
"Verbose : False\n",
"\n",
"Accessible fields : \n",
"m['topics'] : An SFrame containing the topics.\n",
"m['vocabulary'] : An SArray containing the words in the vocabulary.\n",
"Useful methods : \n",
"m.get_topics() : Get the most probable words per topic.\n",
"m.predict(new_docs) : Make predictions for new documents.\n"
]
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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