-
Notifications
You must be signed in to change notification settings - Fork 3
Expand file tree
/
Copy pathenron_blog.html
More file actions
341 lines (306 loc) · 31.6 KB
/
enron_blog.html
File metadata and controls
341 lines (306 loc) · 31.6 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>Network Modeling with the Infinite Relational Model</title>
<link rel="stylesheet" href="_static/basic.css" type="text/css" />
<link rel="stylesheet" href="_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="_static/bootswatch-3.3.4/lumen/bootstrap.min.css" type="text/css" />
<link rel="stylesheet" href="_static/bootstrap-sphinx.css" type="text/css" />
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: './',
VERSION: '0.1.0',
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true
};
</script>
<script type="text/javascript" src="_static/jquery.js"></script>
<script type="text/javascript" src="_static/underscore.js"></script>
<script type="text/javascript" src="_static/doctools.js"></script>
<script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script>
<script type="text/javascript" src="_static/js/jquery-1.11.0.min.js"></script>
<script type="text/javascript" src="_static/js/jquery-fix.js"></script>
<script type="text/javascript" src="_static/bootstrap-3.3.4/js/bootstrap.min.js"></script>
<script type="text/javascript" src="_static/bootstrap-sphinx.js"></script>
<link rel="top" title="None" href="index.html" />
<link rel="up" title="Tutorials" href="docs.html" />
<link rel="next" title="Bayesian Nonparametric Topic Modeling with the Daily Kos" href="topic.html" />
<link rel="prev" title="Finding the number of clusters with the Dirichlet Process" href="ncluster.html" />
<meta charset='utf-8'>
<meta http-equiv='X-UA-Compatible' content='IE=edge,chrome=1'>
<meta name='viewport' content='width=device-width, initial-scale=1.0, maximum-scale=1'>
<meta name="apple-mobile-web-app-capable" content="yes">
</head>
<body role="document">
<div id="navbar" class="navbar navbar-inverse navbar-default navbar-fixed-top">
<div class="container">
<div class="navbar-header">
<!-- .btn-navbar is used as the toggle for collapsed navbar content -->
<button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".nav-collapse">
<span class="icon-bar"></span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="index.html">
datamicroscopes</a>
<span class="navbar-text navbar-version pull-left"><b>0.1</b></span>
</div>
<div class="collapse navbar-collapse nav-collapse">
<ul class="nav navbar-nav">
<li><a href="https://github.com/datamicroscopes">GitHub</a></li>
<li><a href="https://qadium.com/">Qadium</a></li>
<li class="dropdown globaltoc-container">
<a role="button"
id="dLabelGlobalToc"
data-toggle="dropdown"
data-target="#"
href="index.html">Site <b class="caret"></b></a>
<ul class="dropdown-menu globaltoc"
role="menu"
aria-labelledby="dLabelGlobalToc"><ul class="current">
<li class="toctree-l1"><a class="reference internal" href="intro.html">Discovering structure in your data: an overview of clustering</a></li>
<li class="toctree-l1"><a class="reference internal" href="ncluster.html">Finding the number of clusters with the Dirichlet Process</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="">Network Modeling with the Infinite Relational Model</a></li>
<li class="toctree-l1"><a class="reference internal" href="topic.html">Bayesian Nonparametric Topic Modeling with the Daily Kos</a></li>
</ul>
<ul>
<li class="toctree-l1"><a class="reference internal" href="datatypes.html">Datatypes and Bayesian Nonparametric Models</a></li>
<li class="toctree-l1"><a class="reference internal" href="bb.html">Binary Data with the Beta Bernouli Distribution</a></li>
<li class="toctree-l1"><a class="reference internal" href="dd.html">Categorical Data and the Dirichlet Discrete Distribution</a></li>
<li class="toctree-l1"><a class="reference internal" href="niw.html">Real Valued Data and the Normal Inverse-Wishart Distribution</a></li>
<li class="toctree-l1"><a class="reference internal" href="nic.html">Univariate Data with the Normal Inverse Chi-Square Distribution</a></li>
<li class="toctree-l1"><a class="reference internal" href="gamma_poisson.html">Count Data and Ordinal Data with the Gamma-Poisson Distribution</a></li>
</ul>
<ul>
<li class="toctree-l1"><a class="reference internal" href="gauss2d.html">Inferring Gaussians with the Dirichlet Process Mixture Model</a></li>
<li class="toctree-l1"><a class="reference internal" href="mnist_predictions.html">Digit recognition with the MNIST dataset</a></li>
<li class="toctree-l1"><a class="reference internal" href="enron_email.html">Clustering the Enron e-mail corpus using the Infinite Relational Model</a></li>
<li class="toctree-l1"><a class="reference internal" href="hdp.html">Learning Topics in The Daily Kos with the Hierarchical Dirichlet Process</a></li>
</ul>
<ul class="current">
<li class="toctree-l1 current"><a class="reference internal" href="docs.html">Tutorials</a><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="intro.html">Discovering structure in your data: an overview of clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="ncluster.html">Finding the number of clusters with the Dirichlet Process</a></li>
<li class="toctree-l2 current"><a class="current reference internal" href="">Network Modeling with the Infinite Relational Model</a></li>
<li class="toctree-l2"><a class="reference internal" href="topic.html">Bayesian Nonparametric Topic Modeling with the Daily Kos</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="docs.html#datatypes-and-likelihood-models-in-datamicroscopes">Datatypes and likelihood models in datamicroscopes</a><ul>
<li class="toctree-l2"><a class="reference internal" href="datatypes.html">Datatypes and Bayesian Nonparametric Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="bb.html">Binary Data with the Beta Bernouli Distribution</a></li>
<li class="toctree-l2"><a class="reference internal" href="dd.html">Categorical Data and the Dirichlet Discrete Distribution</a></li>
<li class="toctree-l2"><a class="reference internal" href="niw.html">Real Valued Data and the Normal Inverse-Wishart Distribution</a></li>
<li class="toctree-l2"><a class="reference internal" href="nic.html">Univariate Data with the Normal Inverse Chi-Square Distribution</a></li>
<li class="toctree-l2"><a class="reference internal" href="gamma_poisson.html">Count Data and Ordinal Data with the Gamma-Poisson Distribution</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="docs.html#examples">Examples</a><ul>
<li class="toctree-l2"><a class="reference internal" href="gauss2d.html">Inferring Gaussians with the Dirichlet Process Mixture Model</a></li>
<li class="toctree-l2"><a class="reference internal" href="mnist_predictions.html">Digit recognition with the MNIST dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="enron_email.html">Clustering the Enron e-mail corpus using the Infinite Relational Model</a></li>
<li class="toctree-l2"><a class="reference internal" href="hdp.html">Learning Topics in The Daily Kos with the Hierarchical Dirichlet Process</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="api.html">API Reference</a><ul>
<li class="toctree-l2"><a class="reference internal" href="microscopes.common.dataview.html">dataviews</a></li>
<li class="toctree-l2"><a class="reference internal" href="microscopes.common.util.html">util</a></li>
<li class="toctree-l2"><a class="reference internal" href="microscopes.common.random.html">microscopes.common.random</a></li>
<li class="toctree-l2"><a class="reference internal" href="microscopes.common.query.html">query</a></li>
<li class="toctree-l2"><a class="reference internal" href="microscopes.common.validator.html">microscopes.common.validator</a></li>
<li class="toctree-l2"><a class="reference internal" href="microscopes.kernels.parallel.html">parallel</a></li>
<li class="toctree-l2"><a class="reference internal" href="microscopes.mixture.html">mixturemodel</a></li>
<li class="toctree-l2"><a class="reference internal" href="microscopes.irm.html">irm</a></li>
<li class="toctree-l2"><a class="reference internal" href="microscopes.kernels.html">kernels</a></li>
<li class="toctree-l2"><a class="reference internal" href="api.html#indices-and-tables">Indices and tables</a></li>
</ul>
</li>
</ul>
</ul>
</li>
<li class="dropdown">
<a role="button"
id="dLabelLocalToc"
data-toggle="dropdown"
data-target="#"
href="#">Contents <b class="caret"></b></a>
<ul class="dropdown-menu localtoc"
role="menu"
aria-labelledby="dLabelLocalToc"><ul>
<li><a class="reference internal" href="#">Network Modeling with the Infinite Relational Model</a></li>
</ul>
</ul>
</li>
<li class="hidden-sm">
<div id="sourcelink">
<a href="_sources/enron_blog.txt"
rel="nofollow">Source</a>
</div></li>
</ul>
<form class="navbar-form navbar-right" action="search.html" method="get">
<div class="form-group">
<input type="text" name="q" class="form-control" placeholder="Search" />
</div>
<input type="hidden" name="check_keywords" value="yes" />
<input type="hidden" name="area" value="default" />
</form>
</div>
</div>
</div>
<div class="container">
<div class="row">
<div class="col-md-12">
<div class="section" id="network-modeling-with-the-infinite-relational-model">
<span id="enron-blog"></span><h1>Network Modeling with the Infinite Relational Model<a class="headerlink" href="#network-modeling-with-the-infinite-relational-model" title="Permalink to this headline">¶</a></h1>
<hr class="docutils" />
<p>The <a class="reference external" href="http://www.cs.cmu.edu/~./enron/">Enron e-mail corpus</a> contains 500,00 emails between 150 individuals at Enron. To analyze the communication network, we created a binary matrix to represent email
communication between individuals.</p>
<p>In this matrix, <span class="math">\(X_{i,j} = 1\)</span> if and only if person<span class="math">\(_{i}\)</span> sent an email to person<span class="math">\(_{j}\)</span> Note that we are
only recording if an email was ever sent, not the number of emails. Thus our resulting matrix is a binary matrix.</p>
<img alt="_images/enron-email_9_1.png" src="_images/enron-email_9_1.png" />
<p>We’d like to learn what the different classes of people are in the
Enron dataset. Maybe some people (like salespeople) sent a lot of
e-mails outside of the company, and some people (like HR) only sent
e-mails to people inside of the company. Maybe some people received a
lot of e-mail (like bosses) and others received virtually none. We’ll
learn the underlying clusters in this communication matrix using the
Inifinite Relational Model. In this model, the underlying clusters
represents groups of indiviudals in the network based on the kinds of
who they email.</p>
<p>The domain of our model is the individuals in the email dataset.</p>
<p>Our relations are emails between individuals, both of cardinality <span class="math">\(N\)</span>, and we
model the relation with beta-bernoulli distribution since our data is
binary</p>
<div class="highlight-python"><div class="highlight"><pre><span class="n">defn</span> <span class="o">=</span> <span class="n">model_definition</span><span class="p">([</span><span class="n">N</span><span class="p">],</span> <span class="p">[((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">beta_bernoulli</span><span class="p">)])</span>
<span class="n">views</span> <span class="o">=</span> <span class="p">[</span><span class="n">numpy_dataview</span><span class="p">(</span><span class="n">communications_relation</span><span class="p">)]</span>
<span class="n">prng</span> <span class="o">=</span> <span class="n">rng</span><span class="p">()</span>
</pre></div>
</div>
<p>We initialize our model and run a large number of samplers – one
per CPU core.</p>
<div class="highlight-python"><div class="highlight"><pre><span class="n">nchains</span> <span class="o">=</span> <span class="n">cpu_count</span><span class="p">()</span>
<span class="n">latents</span> <span class="o">=</span> <span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">initialize</span><span class="p">(</span><span class="n">defn</span><span class="p">,</span> <span class="n">views</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="n">prng</span><span class="p">,</span> <span class="n">cluster_hps</span><span class="o">=</span><span class="p">[{</span><span class="s">'alpha'</span><span class="p">:</span><span class="mf">1e-3</span><span class="p">}])</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">nchains</span><span class="p">)]</span>
<span class="n">kc</span> <span class="o">=</span> <span class="n">runner</span><span class="o">.</span><span class="n">default_assign_kernel_config</span><span class="p">(</span><span class="n">defn</span><span class="p">)</span>
<span class="n">runners</span> <span class="o">=</span> <span class="p">[</span><span class="n">runner</span><span class="o">.</span><span class="n">runner</span><span class="p">(</span><span class="n">defn</span><span class="p">,</span> <span class="n">views</span><span class="p">,</span> <span class="n">latent</span><span class="p">,</span> <span class="n">kc</span><span class="p">)</span> <span class="k">for</span> <span class="n">latent</span> <span class="ow">in</span> <span class="n">latents</span><span class="p">]</span>
<span class="n">r</span> <span class="o">=</span> <span class="n">parallel</span><span class="o">.</span><span class="n">runner</span><span class="p">(</span><span class="n">runners</span><span class="p">)</span>
</pre></div>
</div>
<p>From here, we can finally run each chain of the sampler 1000 times</p>
<div class="highlight-python"><div class="highlight"><pre><span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">r</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">r</span><span class="o">=</span><span class="n">prng</span><span class="p">,</span> <span class="n">niters</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
<span class="k">print</span> <span class="s">"inference took {} seconds"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span><span class="p">)</span>
</pre></div>
</div>
<p>Now that we have learned our model let’s get our cluster assignments</p>
<div class="highlight-python"><div class="highlight"><pre><span class="n">infers</span> <span class="o">=</span> <span class="n">r</span><span class="o">.</span><span class="n">get_latents</span><span class="p">()</span>
<span class="n">clusters</span> <span class="o">=</span> <span class="n">groups</span><span class="p">(</span><span class="n">infers</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">assignments</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">sort</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">ordering</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">it</span><span class="o">.</span><span class="n">chain</span><span class="o">.</span><span class="n">from_iterable</span><span class="p">(</span><span class="n">clusters</span><span class="p">))</span>
</pre></div>
</div>
<p>Let’s sort the communications matrix to highlight our inferred clusters</p>
<div class="highlight-python"><div class="highlight"><pre><span class="n">z</span> <span class="o">=</span> <span class="n">communications_relation</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">z</span><span class="p">[</span><span class="n">ordering</span><span class="p">]</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">z</span><span class="p">[:,</span><span class="n">ordering</span><span class="p">]</span>
<span class="n">sizes</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span><span class="nb">len</span><span class="p">,</span> <span class="n">clusters</span><span class="p">)</span>
<span class="n">boundaries</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">sizes</span><span class="p">)[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
</pre></div>
</div>
<p>Our model finds suspicious cluster based on the communication data.
We’ll color and label these clusters in our communications matrix.</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">cluster_with_name</span><span class="p">(</span><span class="n">clusters</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">payload</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="n">ident</span> <span class="o">=</span> <span class="n">namemap</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">cluster</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">clusters</span><span class="p">):</span>
<span class="k">if</span> <span class="n">ident</span> <span class="ow">in</span> <span class="n">cluster</span><span class="p">:</span>
<span class="k">return</span> <span class="n">idx</span><span class="p">,</span> <span class="p">(</span><span class="n">cluster</span><span class="p">,</span> <span class="n">payload</span><span class="p">)</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s">"could not find name"</span><span class="p">)</span>
<span class="n">suspicious</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">cluster_with_name</span><span class="p">(</span><span class="n">clusters</span><span class="p">,</span> <span class="s">"horton-s"</span><span class="p">,</span> <span class="p">{</span><span class="s">"color"</span><span class="p">:</span><span class="s">"#66CC66"</span><span class="p">,</span> <span class="s">"desc"</span><span class="p">:</span><span class="s">"The pipeline/regulatory group"</span><span class="p">}),</span>
<span class="n">cluster_with_name</span><span class="p">(</span><span class="n">clusters</span><span class="p">,</span> <span class="s">"skilling-j"</span><span class="p">,</span> <span class="p">{</span><span class="s">"color"</span><span class="p">:</span><span class="s">"#FF6600"</span><span class="p">,</span> <span class="s">"desc"</span><span class="p">:</span><span class="s">"The VIP/executives group"</span><span class="p">}),</span>
<span class="p">]</span>
<span class="n">suspicious</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">suspicious</span><span class="p">)</span>
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="p">(</span><span class="n">boundary</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">boundaries</span><span class="p">,</span> <span class="n">sizes</span><span class="p">)):</span>
<span class="k">if</span> <span class="n">size</span> <span class="o"><</span> <span class="mi">5</span><span class="p">:</span>
<span class="k">continue</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">),</span> <span class="n">boundary</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">N</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s">'#0066CC'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">boundary</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">N</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s">'#0066CC'</span><span class="p">)</span>
<span class="k">if</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">suspicious</span><span class="p">:</span>
<span class="n">rect</span> <span class="o">=</span> <span class="n">patches</span><span class="o">.</span><span class="n">Rectangle</span><span class="p">((</span><span class="n">boundary</span><span class="o">-</span><span class="n">size</span><span class="p">,</span> <span class="n">boundary</span><span class="o">-</span><span class="n">size</span><span class="p">),</span>
<span class="n">width</span><span class="o">=</span><span class="n">size</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="n">size</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">fc</span><span class="o">=</span><span class="n">suspicious</span><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="s">"color"</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">add_patch</span><span class="p">(</span><span class="n">rect</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">blue_cmap</span><span class="p">,</span> <span class="n">interpolation</span><span class="o">=</span><span class="s">'nearest'</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="s">'auto'</span><span class="p">)</span>
<span class="nd">@savefig</span> <span class="n">email_matrix_colored</span><span class="o">.</span><span class="n">png</span> <span class="n">width</span><span class="o">=</span><span class="mi">5</span><span class="ow">in</span>
</pre></div>
</div>
<img alt="_images/enron-email_21_1.png" src="_images/enron-email_21_1.png" />
<p>We’ve identified two suspicious clusters. Let’s look at the data to find
out who these individuals are</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">cluster_names</span><span class="p">(</span><span class="n">cluster</span><span class="p">):</span>
<span class="k">return</span> <span class="p">[</span><span class="n">names</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">cluster</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">get_full_name</span><span class="p">(</span><span class="n">name</span><span class="p">):</span>
<span class="k">return</span> <span class="n">enron_utils</span><span class="o">.</span><span class="n">FULLNAMES</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">get_title</span><span class="p">(</span><span class="n">name</span><span class="p">):</span>
<span class="k">return</span> <span class="n">enron_utils</span><span class="o">.</span><span class="n">TITLES</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="s">"?"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">cluster</span><span class="p">,</span> <span class="n">payload</span> <span class="ow">in</span> <span class="n">suspicious</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
<span class="n">cnames</span> <span class="o">=</span> <span class="n">cluster_names</span><span class="p">(</span><span class="n">cluster</span><span class="p">)</span>
<span class="n">ctitles</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span><span class="n">get_title</span><span class="p">,</span> <span class="n">cnames</span><span class="p">)</span>
<span class="k">print</span> <span class="n">payload</span><span class="p">[</span><span class="s">"desc"</span><span class="p">]</span>
<span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">cnames</span><span class="p">,</span> <span class="n">ctitles</span><span class="p">):</span>
<span class="k">print</span> <span class="s">"</span><span class="se">\t</span><span class="s">"</span><span class="p">,</span> <span class="n">get_full_name</span><span class="p">(</span><span class="n">n</span><span class="p">),</span> <span class="s">'</span><span class="se">\t\t</span><span class="s">"{}"'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
<span class="k">print</span>
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre>The pipeline/regulatory group
Lynn Blair "?"
Shelley Corman "Vice President Regulatory Affairs"
Lindy Donoho "Employee"
Drew Fossum "Vice President"
Tracy Geaccone "Employee"
harris-s "?"
Rod Hayslett "Vice President Also Chief Financial Officer and Treasurer"
Stanley Horton "President Enron Gas Pipeline"
Kevin Hyatt "Director Pipeline Business"
Michelle Lokay "Employee Administrative Asisstant"
Teb Lokey "Manager Regulatory Affairs"
Danny McCarty "Vice President"
mcconnell-m "?"
Darrell Schoolcraft "?"
Kimberly Watson "?"
The VIP/executives group
Rick Buy "Manager Chief Risk Management Officer"
Jeff Dasovich "Employee Government Relation Executive"
David Delainey "CEO Enron North America and Enron Enery Services"
Louise Kitchen "President Enron Online"
John Lavorato "CEO Enron America"
Richard Shapiro "Vice President Regulatory Affairs"
Jeffery Skilling "CEO"
Barry Tycholiz "Vice President"
Greg Whalley "President"
williams-j "?"
</pre></div>
</div>
<p>Given the uncertainty behind these latent clusters, we can visualize the
variablity within these assignments with a z-matrix. Ordering the z-matrix allows us to group members of each possible
cluster together.</p>
<div class="highlight-python"><div class="highlight"><pre><span class="n">zmat</span> <span class="o">=</span> <span class="n">query</span><span class="o">.</span><span class="n">zmatrix</span><span class="p">(</span><span class="n">domain</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">latents</span><span class="o">=</span><span class="n">infers</span><span class="p">)</span>
<span class="n">zmat</span> <span class="o">=</span> <span class="n">zmatrix_reorder</span><span class="p">(</span><span class="n">zmat</span><span class="p">,</span> <span class="n">zmatrix_heuristic_block_ordering</span><span class="p">(</span><span class="n">zmat</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">zmat</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">blue_cmap</span><span class="p">,</span> <span class="n">cbar</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">xticklabels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">yticklabels</span><span class="o">=</span><span class="n">labels</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s">'people (sorted)'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">'people (sorted)'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s">'Z-Matrix of IRM Cluster Assignments'</span><span class="p">)</span>
<span class="nd">@savefig</span> <span class="n">zmatrix</span><span class="o">.</span><span class="n">png</span> <span class="n">width</span><span class="o">=</span><span class="mi">5</span><span class="ow">in</span>
</pre></div>
</div>
<img alt="_images/enron-email_26_1.png" src="_images/enron-email_26_1.png" />
<p>To cluster network data using datamicroscopes, the IRM is available for installation from conda</p>
<div class="highlight-bash"><div class="highlight"><pre><span class="nv">$ </span>conda install microscopes-irm
</pre></div>
</div>
</div>
</div>
</div>
</div>
<!-- your html code here -->
<center> Datamicroscopes is developed by <a href="http://www.qadium.com">Qadium</a>, with funding from the <a href="http://www.darpa.mil">DARPA</a> <a href="http://www.darpa.mil/program/xdata">XDATA</a> program. Copyright Qadium 2015. </center>
</body>
</html>