{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "***\n", "***\n", "# Topic Modeling Using Graphlab\n", "***\n", "***\n", "\n", "王成军\n", "\n", "wangchengjun@nju.edu.cn\n", "\n", "计算传播网 http://computational-communication.com" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "import graphlab\n", "graphlab.canvas.set_target(\"ipynb\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Download Data: http://select.cs.cmu.edu/code/graphlab/datasets/wikipedia/wikipedia_raw/w15" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This non-commercial license of GraphLab Create is assigned to wangchengjun@nju.edu.cn and will expire on July 31, 2016. For commercial licensing options, visit https://dato.com/buy/.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2016-04-14 01:12:14,140 [INFO] graphlab.cython.cy_server, 176: GraphLab Create v1.8.5 started. Logging: /tmp/graphlab_server_1460567529.log\n" ] }, { "data": { "text/html": [ "
Finished parsing file /Users/chengjun/bigdata/w15
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Parsing completed. Parsed 100 lines in 0.546547 secs.
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Finished parsing file /Users/chengjun/bigdata/w15
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Parsing completed. Parsed 72269 lines in 2.23078 secs.
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X1tfidfbow
aynrand born and educated
in russia rand migrated ...		{'limited':
10.04705669672047, ...		{'limited': 3,
'writings': 2, ...
asphalt in american
english asphalt or ...		{'all':
1.3891905239989626, ...		{'all': 1, 'accadian': 1,
'similarity': 1, ...
actinopterygii the
actinopterygii consti ...		{'andreolepis':
11.188150547181156, ...		{'andreolepis': 1, 'all':
1, 'evolutionary': 2, ...
altaiclanguages these
language families share ...		{'sergei':
20.031873121992916, ...		{'sergei': 3, 'all': 6,
'todays': 1, 'chinese': ...
argon the name argon is
derived from the greek ...		{'limited':
3.3490188989068232, ...		{'limited': 1,
'embolism': 1, ...
augustderleth a 1938
guggenheim fellow der ...		{'evelyn':
6.7937013925087175, ...		{'evelyn': 1,
'detective': 4, ...
amateur amateurism can be
seen in both a negative ...		{'since':
1.8775124538896095, ...		{'since': 1, 'subpar': 1,
'lack': 2, 'valuable' ...
assemblyline an assembly
line is a manufacturing ...		{'all':
4.167571571996888, ...		{'all': 3, 'concept': 6,
'consider': 1, 'chine ...
astronomicalunit an
astronomical unit ...		{'precise':
5.491057060675752, 'a ...		{'precise': 1, 'all': 2,
'chinese': 1, 'suns': 1, ...
abbess an abbess latin
abbatissa feminine form ...		{'kildares':
11.188150547181156, ...		{'kildares': 1, 'they':
4, 'founder': 1, ...
\n", "[72269 rows x 3 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n", "
" ], "text/plain": [ "Columns:\n", "\tX1\tstr\n", "\ttfidf\tdict\n", "\tbow\tdict\n", "\n", "Rows: 72269\n", "\n", "Data:\n", "+-------------------------------+-------------------------------+\n", "| X1 | tfidf |\n", "+-------------------------------+-------------------------------+\n", "| aynrand born and educated ... | {'limited': 10.04705669672... |\n", "| asphalt in american englis... | {'all': 1.3891905239989626... |\n", "| actinopterygii the actinop... | {'andreolepis': 11.1881505... |\n", "| altaiclanguages these lang... | {'sergei': 20.031873121992... |\n", "| argon the name argon is de... | {'limited': 3.349018898906... |\n", "| augustderleth a 1938 gugge... | {'evelyn': 6.7937013925087... |\n", "| amateur amateurism can be ... | {'since': 1.87751245388960... |\n", "| assemblyline an assembly l... | {'all': 4.167571571996888,... |\n", "| astronomicalunit an astron... | {'precise': 5.491057060675... |\n", "| abbess an abbess latin abb... | {'kildares': 11.1881505471... |\n", "+-------------------------------+-------------------------------+\n", "+-------------------------------+\n", "| bow |\n", "+-------------------------------+\n", "| {'limited': 3, 'writings':... |\n", "| {'all': 1, 'accadian': 1, ... |\n", "| {'andreolepis': 1, 'all': ... |\n", "| {'sergei': 3, 'all': 6, 't... |\n", "| {'limited': 1, 'embolism':... |\n", "| {'evelyn': 1, 'detective':... |\n", "| {'since': 1, 'subpar': 1, ... |\n", "| {'all': 3, 'concept': 6, '... |\n", "| {'precise': 1, 'all': 2, '... |\n", "| {'kildares': 1, 'they': 4,... |\n", "+-------------------------------+\n", "[72269 rows x 3 columns]\n", "Note: Only the head of the SFrame is printed.\n", "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns." ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sf" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Text cleaning" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "docs = sf['bow'].dict_trim_by_values(2)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "docs = docs.dict_trim_by_keys(graphlab.text_analytics.stopwords(), exclude=True)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Topic modeling" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/html": [ "
Learning a topic model
" ], "text/plain": [ "Learning a topic model" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
       Number of documents     72269
" ], "text/plain": [ " Number of documents 72269" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
           Vocabulary size    171005
" ], "text/plain": [ " Vocabulary size 171005" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
   Running collapsed Gibbs sampling
" ], "text/plain": [ " Running collapsed Gibbs sampling" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
+-----------+---------------+----------------+-----------------+
" ], "text/plain": [ "+-----------+---------------+----------------+-----------------+" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
| Iteration | Elapsed Time  | Tokens/Second  | Est. Perplexity |
" ], "text/plain": [ "| Iteration | Elapsed Time | Tokens/Second | Est. Perplexity |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
+-----------+---------------+----------------+-----------------+
" ], "text/plain": [ "+-----------+---------------+----------------+-----------------+" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
| 10        | 2.48s         | 8.92734e+06    | 0               |
" ], "text/plain": [ "| 10 | 2.48s | 8.92734e+06 | 0 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
+-----------+---------------+----------------+-----------------+
" ], "text/plain": [ "+-----------+---------------+----------------+-----------------+" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m = graphlab.topic_model.create(docs)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "Class : TopicModel\n", "\n", "Schema\n", "------\n", "Vocabulary Size : 171005\n", "\n", "Settings\n", "--------\n", "Number of Topics : 10\n", "alpha : 5.0\n", "beta : 0.1\n", "Iterations : 10\n", "Training time : 3.4936\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." ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/html": [ "
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topicwordscore
0series0.018582602707
0time0.0160412461512
0played0.0142993990545
0back0.00951875933204
0game0.00839911774869
1war0.0176185833315
1film0.0159278169528
1group0.0140632734063
1party0.0103356107163
1year0.0102957274319
\n", "[50 rows x 3 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n", "
" ], "text/plain": [ "Columns:\n", "\ttopic\tint\n", "\tword\tstr\n", "\tscore\tfloat\n", "\n", "Rows: 50\n", "\n", "Data:\n", "+-------+--------+------------------+\n", "| topic | word | score |\n", "+-------+--------+------------------+\n", "| 0 | series | 0.018582602707 |\n", "| 0 | time | 0.0160412461512 |\n", "| 0 | played | 0.0142993990545 |\n", "| 0 | back | 0.00951875933204 |\n", "| 0 | game | 0.00839911774869 |\n", "| 1 | war | 0.0176185833315 |\n", "| 1 | film | 0.0159278169528 |\n", "| 1 | group | 0.0140632734063 |\n", "| 1 | party | 0.0103356107163 |\n", "| 1 | year | 0.0102957274319 |\n", "+-------+--------+------------------+\n", "[50 rows x 3 columns]\n", "Note: Only the head of the SFrame is printed.\n", "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns." ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.get_topics()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['series', 'game', 'time', 'back', 'played']\n", "['club', 'city', 'work', 'season', 'league']\n", "['party', 'group', 'war', 'film', 'year']\n", "['years', 'university', 'law', 'year', 'time']\n", "['town', 'age', 'system', 'south', 'church']\n", "['school', 'de', 'river', 'family', 'century']\n", "['world', 'national', 'including', 'number', 'team']\n", "['states', 'city', 'state', 'population', 'government']\n", "['album', 'band', 'song', 'music', 'released']\n", "['land', 'game', 'army', 'local', 'area']\n" ] } ], "source": [ "topics = m.get_topics().unstack(['word','score'], new_column_name='topic_words')['topic_words'].apply(lambda x: x.keys())\n", "for topic in topics:\n", " print topic" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "pred = m.predict(docs)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "application/javascript": [ "$(\"head\").append($(\"\").attr({\n", " rel: \"stylesheet\",\n", " type: \"text/css\",\n", " href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n", "}));\n", "$(\"head\").append($(\"\").attr({\n", " rel: \"stylesheet\",\n", " type: \"text/css\",\n", " href: \"//dato.com/files/canvas/1.8.5/css/canvas.css\"\n", "}));\n", "\n", " (function(){\n", "\n", " var e = null;\n", " if (typeof element == 'undefined') {\n", " var scripts = document.getElementsByTagName('script');\n", " var thisScriptTag = scripts[scripts.length-1];\n", " var parentDiv = thisScriptTag.parentNode;\n", " e = document.createElement('div');\n", " parentDiv.appendChild(e);\n", " } else {\n", " e = element[0];\n", " }\n", "\n", " if (typeof requirejs !== 'undefined') {\n", " // disable load timeout; ipython_app.js is large and can take a while to load.\n", " requirejs.config({waitSeconds: 0});\n", " }\n", "\n", " require(['//dato.com/files/canvas/1.8.5/js/ipython_app.js'], function(IPythonApp){\n", " var app = new IPythonApp();\n", " app.attachView('sarray','Numeric', {\"ipython\": true, \"sketch\": {\"std\": 3.0219703958967616, \"complete\": true, \"min\": 0.0, \"max\": 9.0, \"quantile\": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0], \"median\": 6.0, \"numeric\": true, \"num_unique\": 10, \"num_undefined\": 0, \"var\": 9.13230507367643, \"progress\": 1.0, \"size\": 72269, \"frequent_items\": {\"0\": {\"frequency\": 5058, \"value\": 0}, \"1\": {\"frequency\": 5683, \"value\": 1}, \"2\": {\"frequency\": 5996, \"value\": 2}, \"3\": {\"frequency\": 6240, \"value\": 3}, \"4\": {\"frequency\": 5101, \"value\": 4}, \"5\": {\"frequency\": 7852, \"value\": 5}, \"6\": {\"frequency\": 6396, \"value\": 6}, \"7\": {\"frequency\": 5280, \"value\": 7}, \"8\": {\"frequency\": 7931, \"value\": 8}, \"9\": {\"frequency\": 16732, \"value\": 9}}, \"mean\": 5.333282596964117}, \"selected_variable\": {\"name\": [\"\"], \"dtype\": \"int\", \"view_component\": \"Numeric\", \"view_file\": \"sarray\", \"descriptives\": {\"rows\": 72269}, \"type\": \"SArray\", \"view_components\": [\"Numeric\", \"Categorical\"]}, \"histogram\": {\"progress\": 1.0, \"histogram\": {\"max\": 9.02, \"bins\": [5058, 5683, 5996, 6240, 0, 5101, 7852, 6396, 0, 5280, 7931, 16732], \"min\": -0.0040000000000000036}, \"min\": 0, \"complete\": 1, \"max\": 9}}, e);\n", " });\n", " })();\n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pred.show()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "pred = m.predict(docs, output_type='probabilities')" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "dtype: str\n", "Rows: 171005\n", "['duke', 'studies', 'journal', 'chris', 'research', 'matthew', 'crisis', 'financial', 'paul', '1987', 'reagan', 'traditional', 'rightwing', 'nominee', 'libertarianism', 'cato', 'chief', 'smith', 'line', 'south', 'nick', '1999', 'documentary', 'animated', 'shows', 'references', 'commentator', 'powerful', 'ethics', 'rush', 'neil', 'lives', 'cited', 'produced', 'night', 'originality', 'interest', '2007', 'individual', 'authors', 'admirer', 'married', 'club', 'library', 'essays', 'recent', '2009', 'burns', 'inspiration', 'artist', 'women', 'early', 'barbara', 'organized', 'gave', 'referred', 'company', 'personalist', 'criticism', 'john', 'reviewers', 'language', 'understanding', 'writes', 'fewer', 'attention', 'positive', 'masterful', 'review', 'times', 'critic', 'praise', 'theory', 'randian', 'importance', 'calling', 'nonfiction', 'academics', 'kant', 'philosophers', 'italian', 'remarked', 'wife', 'house', 'subject', 'scholarly', 'edward', 'system', 'influence', 'acknowledged', '100', 'branden', 'criticized', 'sacrificing', 'exist', 'selfinterest', 'rational', 'communism', 'journals', 'copies', ... ]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m['vocabulary']" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/html": [ "
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topic_probabilitiesvocabulary
[1.6417032014e-07,
1.42440301489e-07, ...		duke
[1.6417032014e-07,
1.42440301489e-07, ...		studies
[1.6417032014e-07,
1.42440301489e-07, ...		journal
[1.6417032014e-07,
1.42440301489e-07, ...		chris
[1.6417032014e-07,
1.42440301489e-07, ...		research
[0.000305520965781,
1.42440301489e-07, ...		matthew
[3.44757672294e-06,
1.42440301489e-07, ...		crisis
[1.6417032014e-07,
4.41564934616e-06, ...		financial
[1.6417032014e-07,
1.42440301489e-07, ...		paul
[1.6417032014e-07,
0.00033772595483, ...		1987
\n", "[171005 rows x 2 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n", "
" ], "text/plain": [ "Columns:\n", "\ttopic_probabilities\tarray\n", "\tvocabulary\tstr\n", "\n", "Rows: 171005\n", "\n", "Data:\n", "+-------------------------------+------------+\n", "| topic_probabilities | vocabulary |\n", "+-------------------------------+------------+\n", "| [1.6417032014e-07, 1.42440... | duke |\n", "| [1.6417032014e-07, 1.42440... | studies |\n", "| [1.6417032014e-07, 1.42440... | journal |\n", "| [1.6417032014e-07, 1.42440... | chris |\n", "| [1.6417032014e-07, 1.42440... | research |\n", "| [0.000305520965781, 1.4244... | matthew |\n", "| [3.44757672294e-06, 1.4244... | crisis |\n", "| [1.6417032014e-07, 4.41564... | financial |\n", "| [1.6417032014e-07, 1.42440... | paul |\n", "| [1.6417032014e-07, 0.00033... | 1987 |\n", "+-------------------------------+------------+\n", "[171005 rows x 2 columns]\n", "Note: Only the head of the SFrame is printed.\n", "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns." ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m['topics']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "def print_topics(m):\n", " topics = m.get_topics(num_words=5)\n", " topics = topics.unstack(['word','score'], new_column_name='topic_words')['topic_words']\n", " topics = topics.apply(lambda x: x.keys())\n", " for topic in topics:\n", " print topic\n", "print_topics(m)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Initializing from other models" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "m2 = graphlab.topic_model.create(docs,\n", " num_topics=20,\n", " initial_topics=m['topics'])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Seeding the model with prior knowledge" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "associations = graphlab.SFrame()\n", "associations['word'] = ['recognition']\n", "associations['topic'] = [0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "m2 = graphlab.topic_model.create(docs,\n", " num_topics=20,\n", " num_iterations=50,\n", " associations=associations, \n", " verbose=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "m2.get_topics(num_words=10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "print_topics(m2)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "source": [ "# 阅读材料\n", "\n", "https://dato.com/learn/userguide/text/topic-models.html" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Slideshow", "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 }