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tdhopper/HDP-LDA Demo.ipynb

Created July 17, 2015 14:31
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HDP-LDA Demo.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from microscopes.common.rng import rng\n",
"from microscopes.lda.definition import model_definition\n",
"from microscopes.lda.model import initialize\n",
"from microscopes.lda.testutil import toy_dataset\n",
"from microscopes.lda import model, runner\n",
"from collections import Counter\n",
"\n",
"import itertools\n",
"import numpy as np\n",
"import pyLDAvis\n",
"import scipy as sp\n",
"import numpy as np\n",
"import re\n",
"import simplejson"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Topics Found: 3\n",
"Assignments:\n",
"\t[1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
"\t[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
"\t[2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2]\n",
"\t[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
"\t[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
"\t[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]\n",
"\t[2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2]\n",
"\t[3, 1, 1, 1, 3, 1, 1, 3, 1, 3, 1, 1]\n",
"\t[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
"\t[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
]
}
],
"source": [
"N, V = 10, 3\n",
"defn = model_definition(N, V)\n",
"data = toy_dataset(defn)\n",
"prng = rng()\n",
"\n",
"latent = initialize(defn, data, prng)\n",
"r = runner.runner(defn, data, latent)\n",
"r.run(prng, 1000)\n",
"\n",
"print \"Topics Found:\", latent.ntopics()\n",
"print \"Assignments:\"\n",
"for doc in latent.assignments():\n",
" print \"\\t\", doc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualize Daily Kos Topics"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_vis_data(latent, num_docs, num_to_word):\n",
" sorted_num_vocab = sorted(num_to_word.keys())\n",
" \n",
" topic_term_distribution = []\n",
" for topic in latent.word_distribution(prng):\n",
" topic_term_distribution.append([topic[word_id] for word_id in sorted_num_vocab])\n",
" \n",
" doc_topic_distribution = latent.document_distribution()\n",
" \n",
" doc_lengths = [len(doc) for doc in num_docs]\n",
" \n",
" vocab = [num_to_word[k] for k in sorted_num_vocab]\n",
" assert all(map(len, vocab))\n",
" \n",
" ctr = Counter(list(itertools.chain.from_iterable(num_docs)))\n",
" term_frequency = [ctr[num] for num in sorted_num_vocab]\n",
" \n",
" return {'topic_term_dists': topic_term_distribution, \n",
" 'doc_topic_dists': doc_topic_distribution,\n",
" 'doc_lengths': doc_lengths,\n",
" 'vocab': vocab,\n",
" 'term_frequency': term_frequency}"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with open(\"docword.kos.txt\", \"r\") as f:\n",
" kos_raw = [map(int, _.strip().split()) for _ in f.readlines()][3:]\n",
"\n",
" docs = []\n",
"for _, grp in itertools.groupby(kos_raw, lambda x: x[0]):\n",
" doc = []\n",
" for _, word_id, word_cnt in grp:\n",
" doc += word_cnt * [word_id - 1]\n",
" docs.append(doc)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with open(\"vocab.kos.txt\", \"r\") as f:\n",
" kos_vocab = [word.strip() for word in f.readlines()]\n",
"id_to_word = {i: word for i, word in enumerate(kos_vocab)}\n",
"word_to_id = {word: i for i, word in enumerate(kos_vocab)}"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of docs: 3430 vocabulary size: 6906\n"
]
}
],
"source": [
"N, V = len(docs), len(id_to_word)\n",
"defn = model_definition(N, V)\n",
"prng = rng()\n",
"latent = initialize(defn, docs, prng, \n",
" vocab_hp=0.5, \n",
" dish_hps={\"alpha\": 0.1, \"gamma\": 0.1})\n",
"r = runner.runner(defn, docs, latent)\n",
"\n",
"print \"number of docs:\", N, \"vocabulary size:\", V"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"iteration: 0 perplexity: 1660.04514575 num topics: 13\n",
"iteration: 10 perplexity: 1642.88840381 num topics: 14\n",
"iteration: 20 perplexity: 1628.16751916 num topics: 14\n",
"iteration: 30 perplexity: 1621.08824537 num topics: 13\n",
"iteration: 40 perplexity: 1613.7293193 num topics: 14\n",
"iteration: 50 perplexity: 1607.56607523 num topics: 14\n",
"iteration: 60 perplexity: 1598.09713799 num topics: 14\n",
"iteration: 70 perplexity: 1584.36558045 num topics: 14\n",
"iteration: 80 perplexity: 1580.50486935 num topics: 14\n",
"iteration: 90 perplexity: 1577.8944629 num topics: 15\n",
"iteration: 100 perplexity: 1574.40470209 num topics: 15\n",
"iteration: 110 perplexity: 1571.62380585 num topics: 15\n",
"iteration: 120 perplexity: 1570.25337662 num topics: 15\n",
"iteration: 130 perplexity: 1560.67636844 num topics: 16\n",
"iteration: 140 perplexity: 1556.59002989 num topics: 17\n",
"iteration: 150 perplexity: 1555.3368302 num topics: 16\n",
"iteration: 160 perplexity: 1553.96941592 num topics: 16\n",
"iteration: 170 perplexity: 1552.24021544 num topics: 16\n",
"iteration: 180 perplexity: 1550.72215895 num topics: 17\n",
"iteration: 190 perplexity: 1548.90044365 num topics: 17\n"
]
}
],
"source": [
"step_size = 10\n",
"steps = 20\n",
"\n",
"for _ in range(steps):\n",
" r.run(prng, step_size)\n",
" with open(\"daily-kos-summary.json\", \"w\") as fp:\n",
" simplejson.dump(get_vis_data(latent, docs, id_to_word), fp=fp)\n",
" print \"iteration:\", _ * step_size, \"perplexity:\", latent.perplexity(), \"num topics:\", latent.ntopics()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"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_el4065744266144809611862881\"></div>\n",
"<script type=\"text/javascript\">\n",
"\n",
"var ldavis_el4065744266144809611862881_data = {\"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [2, 4, 5, 6, 1, 9, 3, 7, 10, 15, 8, 12, 14, 13, 17, 16, 11], \"token.table\": {\"Topic\": [7, 9, 1, 2, 3, 4, 8, 4, 7, 8, 2, 3, 5, 6, 9, 1, 2, 6, 9, 1, 3, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9, 13, 1, 2, 3, 4, 7, 8, 9, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 14, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 1, 4, 5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 4, 7, 8, 9, 11, 1, 2, 4, 5, 8, 10, 1, 2, 4, 5, 6, 7, 8, 1, 3, 6, 7, 2, 1, 7, 1, 2, 3, 4, 5, 6, 7, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 4, 9, 11, 1, 3, 6, 7, 2, 3, 4, 13, 1, 5, 1, 1, 5, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 6, 7, 8, 9, 10, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 10, 4, 5, 9, 3, 4, 9, 12, 1, 2, 3, 4, 6, 13, 2, 3, 6, 1, 2, 5, 6, 1, 2, 8, 9, 1, 4, 6, 9, 1, 2, 3, 4, 5, 8, 11, 1, 3, 7, 10, 1, 2, 4, 5, 6, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9, 15, 1, 2, 3, 4, 5, 10, 11, 2, 3, 4, 5, 7, 8, 1, 3, 4, 5, 1, 2, 4, 8, 13, 1, 2, 3, 8, 12, 1, 2, 4, 5, 6, 7, 9, 1, 2, 3, 4, 6, 8, 1, 2, 3, 5, 6, 7, 8, 9, 10, 1, 2, 3, 5, 7, 1, 2, 3, 4, 5, 7, 8, 9, 10, 14, 1, 2, 3, 4, 5, 6, 7, 8, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 4, 7, 8, 11, 2, 4, 5, 6, 9, 1, 2, 4, 5, 8, 1, 2, 3, 4, 7, 5, 6, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 1, 11, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 9, 1, 3, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 9, 10, 1, 3, 4, 5, 6, 8, 9, 16, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 3, 1, 2, 3, 4, 6, 7, 8, 9, 1, 3, 5, 6, 8, 12, 1, 2, 3, 4, 7, 8, 9, 10, 12, 1, 2, 3, 4, 6, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 16, 1, 2, 3, 4, 5, 7, 8, 10, 1, 3, 4, 5, 6, 7, 1, 2, 8, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 1, 3, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 14, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 6, 8, 1, 2, 4, 6, 1, 2, 3, 4, 6, 7, 8, 9, 5, 2, 3, 4, 7, 9, 4, 1, 1, 2, 3, 4, 6, 7, 8, 9, 1, 2, 3, 4, 8, 1, 2, 3, 4, 6, 7, 1, 2, 3, 9, 1, 5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 17, 1, 5, 7, 1, 5, 5, 1, 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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_el4065744266144809611862881\", ldavis_el4065744266144809611862881_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_el4065744266144809611862881\", ldavis_el4065744266144809611862881_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_el4065744266144809611862881\", ldavis_el4065744266144809611862881_data);\n",
" })\n",
" });\n",
"}\n",
"</script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = get_vis_data(latent, docs, id_to_word)\n",
"prepared = pyLDAvis.prepare(**data)\n",
"pyLDAvis.display(prepared)"
]
},
{
"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": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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