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<div class="section" id="learning-topics-in-the-daily-kos-with-the-hierarchical-dirichlet-process">
<h1>Learning Topics in The Daily Kos with the Hierarchical Dirichlet Process<a class="headerlink" href="#learning-topics-in-the-daily-kos-with-the-hierarchical-dirichlet-process" title="Permalink to this headline">¶</a></h1>
<p>The Hierarchical Dirichlet Process (HDP) is typically used for topic
modeling when the number of topics is unknown</p>
<p>Let’s explore the topics of the political blog, The Daily Kos</p>
<div class="code python highlight-python"><div class="highlight"><pre><span class="kn">from</span> <span class="nn">microscopes.common.rng</span> <span class="kn">import</span> <span class="n">rng</span>
<span class="kn">from</span> <span class="nn">microscopes.lda.definition</span> <span class="kn">import</span> <span class="n">model_definition</span>
<span class="kn">from</span> <span class="nn">microscopes.lda.model</span> <span class="kn">import</span> <span class="n">initialize</span>
<span class="kn">from</span> <span class="nn">microscopes.lda.testutil</span> <span class="kn">import</span> <span class="n">toy_dataset</span>
<span class="kn">from</span> <span class="nn">microscopes.lda</span> <span class="kn">import</span> <span class="n">model</span><span class="p">,</span> <span class="n">runner</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">Counter</span>
<span class="kn">import</span> <span class="nn">itertools</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pyLDAvis</span>
<span class="kn">import</span> <span class="nn">scipy</span> <span class="kn">as</span> <span class="nn">sp</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">re</span>
<span class="kn">import</span> <span class="nn">simplejson</span>
</pre></div>
</div>
<p>We will visualize these topics with
<a class="reference external" href="https://github.com/bmabey/pyLDAvis">pyLDAvis</a>. To prep the data,
we’ll create a function:</p>
<div class="code python highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">get_vis_data</span><span class="p">(</span><span class="n">latent</span><span class="p">,</span> <span class="n">num_docs</span><span class="p">,</span> <span class="n">num_to_word</span><span class="p">):</span>
<span class="n">sorted_num_vocab</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">num_to_word</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="n">topic_term_distribution</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">topic</span> <span class="ow">in</span> <span class="n">latent</span><span class="o">.</span><span class="n">word_distribution</span><span class="p">(</span><span class="n">prng</span><span class="p">):</span>
<span class="n">topic_term_distribution</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">topic</span><span class="p">[</span><span class="n">word_id</span><span class="p">]</span> <span class="k">for</span> <span class="n">word_id</span> <span class="ow">in</span> <span class="n">sorted_num_vocab</span><span class="p">])</span>
<span class="n">doc_topic_distribution</span> <span class="o">=</span> <span class="n">latent</span><span class="o">.</span><span class="n">document_distribution</span><span class="p">()</span>
<span class="n">doc_lengths</span> <span class="o">=</span> <span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">doc</span><span class="p">)</span> <span class="k">for</span> <span class="n">doc</span> <span class="ow">in</span> <span class="n">num_docs</span><span class="p">]</span>
<span class="n">vocab</span> <span class="o">=</span> <span class="p">[</span><span class="n">num_to_word</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">sorted_num_vocab</span><span class="p">]</span>
<span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">len</span><span class="p">,</span> <span class="n">vocab</span><span class="p">))</span>
<span class="n">ctr</span> <span class="o">=</span> <span class="n">Counter</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">itertools</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">num_docs</span><span class="p">)))</span>
<span class="n">term_frequency</span> <span class="o">=</span> <span class="p">[</span><span class="n">ctr</span><span class="p">[</span><span class="n">num</span><span class="p">]</span> <span class="k">for</span> <span class="n">num</span> <span class="ow">in</span> <span class="n">sorted_num_vocab</span><span class="p">]</span>
<span class="k">return</span> <span class="p">{</span><span class="s">'topic_term_dists'</span><span class="p">:</span> <span class="n">topic_term_distribution</span><span class="p">,</span>
<span class="s">'doc_topic_dists'</span><span class="p">:</span> <span class="n">doc_topic_distribution</span><span class="p">,</span>
<span class="s">'doc_lengths'</span><span class="p">:</span> <span class="n">doc_lengths</span><span class="p">,</span>
<span class="s">'vocab'</span><span class="p">:</span> <span class="n">vocab</span><span class="p">,</span>
<span class="s">'term_frequency'</span><span class="p">:</span> <span class="n">term_frequency</span><span class="p">}</span>
</pre></div>
</div>
<p>Daily Kos data is stored locally. We’ll process the data into a list of
lists:</p>
<div class="code python highlight-python"><div class="highlight"><pre><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s">"docword.kos.txt"</span><span class="p">,</span> <span class="s">"r"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">kos_raw</span> <span class="o">=</span> <span class="p">[</span><span class="nb">map</span><span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="n">_</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">())</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">f</span><span class="o">.</span><span class="n">readlines</span><span class="p">()][</span><span class="mi">3</span><span class="p">:]</span>
<span class="n">docs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">grp</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">kos_raw</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
<span class="n">doc</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">word_id</span><span class="p">,</span> <span class="n">word_cnt</span> <span class="ow">in</span> <span class="n">grp</span><span class="p">:</span>
<span class="n">doc</span> <span class="o">+=</span> <span class="n">word_cnt</span> <span class="o">*</span> <span class="p">[</span><span class="n">word_id</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
<span class="n">docs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">doc</span><span class="p">)</span>
</pre></div>
</div>
<p>The data is stored as indices of words in the vocabulary. We’ll turn
these indices and words into dictionaries as a reference.</p>
<div class="code python highlight-python"><div class="highlight"><pre><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s">"vocab.kos.txt"</span><span class="p">,</span> <span class="s">"r"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">kos_vocab</span> <span class="o">=</span> <span class="p">[</span><span class="n">word</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span> <span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">f</span><span class="o">.</span><span class="n">readlines</span><span class="p">()]</span>
<span class="n">id_to_word</span> <span class="o">=</span> <span class="p">{</span><span class="n">i</span><span class="p">:</span> <span class="n">word</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">word</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">kos_vocab</span><span class="p">)}</span>
<span class="n">word_to_id</span> <span class="o">=</span> <span class="p">{</span><span class="n">word</span><span class="p">:</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">word</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">kos_vocab</span><span class="p">)}</span>
</pre></div>
</div>
<p>We must define our model before we intialize it. In this case, we need
the number of docs and the number of words.</p>
<p>From there, we can initialize our model and set the hyperparameters</p>
<div class="code python highlight-python"><div class="highlight"><pre><span class="n">N</span><span class="p">,</span> <span class="n">V</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">docs</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">id_to_word</span><span class="p">)</span>
<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="n">V</span><span class="p">)</span>
<span class="n">prng</span> <span class="o">=</span> <span class="n">rng</span><span class="p">()</span>
<span class="n">kos_latent</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">docs</span><span class="p">,</span> <span class="n">prng</span><span class="p">,</span>
<span class="n">vocab_hp</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">dish_hps</span><span class="o">=</span><span class="p">{</span><span class="s">"alpha"</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">,</span> <span class="s">"gamma"</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">})</span>
<span class="n">r</span> <span class="o">=</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">docs</span><span class="p">,</span> <span class="n">kos_latent</span><span class="p">)</span>
<span class="k">print</span> <span class="s">"number of docs:"</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="s">"vocabulary size:"</span><span class="p">,</span> <span class="n">V</span>
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre>number of docs: 3430 vocabulary size: 6906
</pre></div>
</div>
<div class="code python highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">check_params</span><span class="p">(</span><span class="n">alpha</span><span class="p">,</span> <span class="n">gamma</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">docs</span><span class="p">),</span> <span class="n">v</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">id_to_word</span><span class="p">)):</span>
<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="n">v</span><span class="p">)</span>
<span class="n">prng</span> <span class="o">=</span> <span class="n">rng</span><span class="p">()</span>
<span class="n">kos_latent</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">docs</span><span class="p">,</span> <span class="n">prng</span><span class="p">,</span>
<span class="n">vocab_hp</span><span class="o">=</span><span class="n">eta</span><span class="p">,</span>
<span class="n">dish_hps</span><span class="o">=</span><span class="p">{</span><span class="s">"alpha"</span><span class="p">:</span> <span class="n">alpha</span><span class="p">,</span> <span class="s">"gamma"</span><span class="p">:</span> <span class="n">gamma</span><span class="p">})</span>
<span class="n">r</span> <span class="o">=</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">docs</span><span class="p">,</span> <span class="n">kos_latent</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">prng</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="k">print</span> <span class="n">kos_latent</span><span class="o">.</span><span class="n">ntopics</span><span class="p">()</span>
<span class="k">return</span> <span class="n">kos_latent</span>
</pre></div>
</div>
<p>Given the size of the dataset, it’ll take some time to run.</p>
<p>We’ll run our model for 1000 iterations and save our results every 25
iterations.</p>
<div class="code python highlight-python"><div class="highlight"><pre><span class="n">step_size</span> <span class="o">=</span> <span class="mi">25</span>
<span class="n">steps</span> <span class="o">=</span> <span class="mi">40</span>
<span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">steps</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">prng</span><span class="p">,</span> <span class="n">step_size</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s">"daily-kos-summary.json"</span><span class="p">,</span> <span class="s">"w"</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
<span class="n">simplejson</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">get_vis_data</span><span class="p">(</span><span class="n">kos_latent</span><span class="p">,</span> <span class="n">docs</span><span class="p">,</span> <span class="n">id_to_word</span><span class="p">),</span> <span class="n">fp</span><span class="o">=</span><span class="n">fp</span><span class="p">)</span>
<span class="k">print</span> <span class="s">"iteration:"</span><span class="p">,</span> <span class="n">s</span> <span class="o">*</span> <span class="n">step_size</span><span class="p">,</span> <span class="s">"perplexity:"</span><span class="p">,</span> <span class="n">kos_latent</span><span class="o">.</span><span class="n">perplexity</span><span class="p">(),</span> <span class="s">"num topics:"</span><span class="p">,</span> <span class="n">kos_latent</span><span class="o">.</span><span class="n">ntopics</span><span class="p">()</span>
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre>iteration: 0 perplexity: 1862.04247596 num topics: 7
iteration: 25 perplexity: 1841.41178003 num topics: 7
iteration: 50 perplexity: 1822.86140826 num topics: 7
iteration: 75 perplexity: 1811.27293604 num topics: 7
iteration: 100 perplexity: 1801.32272475 num topics: 8
iteration: 125 perplexity: 1793.89339826 num topics: 7
iteration: 150 perplexity: 1784.2086994 num topics: 8
iteration: 175 perplexity: 1775.26781672 num topics: 9
iteration: 200 perplexity: 1771.0568549 num topics: 10
iteration: 225 perplexity: 1767.12303448 num topics: 9
iteration: 250 perplexity: 1765.20349732 num topics: 8
iteration: 275 perplexity: 1762.72988472 num topics: 9
iteration: 300 perplexity: 1761.0224189 num topics: 9
iteration: 325 perplexity: 1759.81190482 num topics: 10
iteration: 350 perplexity: 1757.97894703 num topics: 11
iteration: 375 perplexity: 1758.01621354 num topics: 9
iteration: 400 perplexity: 1756.7352334 num topics: 10
iteration: 425 perplexity: 1755.64540322 num topics: 9
iteration: 450 perplexity: 1755.18156273 num topics: 11
iteration: 475 perplexity: 1755.08042104 num topics: 10
iteration: 500 perplexity: 1752.93599661 num topics: 11
iteration: 525 perplexity: 1752.0584398 num topics: 10
iteration: 550 perplexity: 1751.00181064 num topics: 9
iteration: 575 perplexity: 1749.03641521 num topics: 10
iteration: 600 perplexity: 1747.96555338 num topics: 9
iteration: 625 perplexity: 1745.90262138 num topics: 10
iteration: 650 perplexity: 1743.19619415 num topics: 9
iteration: 675 perplexity: 1739.90203233 num topics: 10
iteration: 700 perplexity: 1736.04214534 num topics: 10
iteration: 725 perplexity: 1733.022819 num topics: 10
iteration: 750 perplexity: 1722.42097487 num topics: 11
iteration: 775 perplexity: 1716.93771047 num topics: 11
iteration: 800 perplexity: 1712.08908449 num topics: 10
iteration: 825 perplexity: 1708.83194332 num topics: 10
iteration: 850 perplexity: 1706.28896225 num topics: 12
iteration: 875 perplexity: 1704.94940029 num topics: 10
iteration: 900 perplexity: 1703.74706636 num topics: 9
iteration: 925 perplexity: 1701.05461971 num topics: 10
iteration: 950 perplexity: 1700.10961614 num topics: 10
iteration: 975 perplexity: 1697.12639527 num topics: 10
</pre></div>
</div>
<p>Since inference takes a long time, we can load our trained data in this
block if we want to visualize it later. We will now load our data and
visualize our topics with
<a class="reference external" href="https://github.com/bmabey/pyLDAvis">pyLDAvis</a></p>
<div class="code python highlight-python"><div class="highlight"><pre><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s">"daily-kos-summary.json"</span><span class="p">,</span> <span class="s">"r"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">text</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">simplejson</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">text</span><span class="p">)</span>
<span class="n">prepared</span> <span class="o">=</span> <span class="n">pyLDAvis</span><span class="o">.</span><span class="n">prepare</span><span class="p">(</span><span class="o">**</span><span class="n">data</span><span class="p">)</span>
<span class="n">pyLDAvis</span><span class="o">.</span><span class="n">display</span><span class="p">(</span><span class="n">prepared</span><span class="p">)</span>
</pre></div>
</div>
<link rel="stylesheet" type="text/css" href="https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css">
<div id="ldavis_el2934345989676325306695865"></div>
<script type="text/javascript">
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