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<li><a class="reference internal" href="#">Topics extraction with Non-Negative Matrix Factorization</a></li>
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<div class="section" id="topics-extraction-with-non-negative-matrix-factorization">
<span id="example-applications-topics-extraction-with-nmf-py"></span><h1>Topics extraction with Non-Negative Matrix Factorization<a class="headerlink" href="#topics-extraction-with-non-negative-matrix-factorization" title="Permalink to this headline">¶</a></h1>
<p>This is a proof of concept application of Non Negative Matrix
Factorization of the term frequency matrix of a corpus of documents so
as to extract an additive model of the topic structure of the corpus.
The output is a list of topics, each represented as a list of terms
(weights are not shown).</p>
<p>The default parameters (n_samples / n_features / n_topics) should make
the example runnable in a couple of tens of seconds. You can try to
increase the dimensions of the problem, but be aware than the time complexity
is polynomial.</p>
<p><strong>Python source code:</strong> <a class="reference download internal" href="../../_downloads/topics_extraction_with_nmf.py"><tt class="xref download docutils literal"><span class="pre">topics_extraction_with_nmf.py</span></tt></a></p>
<div class="highlight-python"><div class="highlight"><pre><span class="c"># Author: Olivier Grisel <[email protected]></span>
<span class="c"># Lars Buitinck <[email protected]></span>
<span class="c"># License: BSD 3 clause</span>
<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">time</span>
<span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer"><span class="n">TfidfVectorizer</span></a>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF"><span class="n">NMF</span></a>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_20newsgroups</span>
<span class="n">n_samples</span> <span class="o">=</span> <span class="mi">2000</span>
<span class="n">n_features</span> <span class="o">=</span> <span class="mi">1000</span>
<span class="n">n_topics</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">n_top_words</span> <span class="o">=</span> <span class="mi">20</span>
<span class="c"># Load the 20 newsgroups dataset and vectorize it. We use a few heuristics</span>
<span class="c"># to filter out useless terms early on: the posts are stripped of headers,</span>
<span class="c"># footers and quoted replies, and common English words, words occurring in</span>
<span class="c"># only one document or in at least 95% of the documents are removed.</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="k">print</span><span class="p">(</span><span class="s">"Loading dataset and extracting TF-IDF features..."</span><span class="p">)</span>
<span class="n">dataset</span> <span class="o">=</span> <span class="n">fetch_20newsgroups</span><span class="p">(</span><span class="n">shuffle</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">remove</span><span class="o">=</span><span class="p">(</span><span class="s">'headers'</span><span class="p">,</span> <span class="s">'footers'</span><span class="p">,</span> <span class="s">'quotes'</span><span class="p">))</span>
<span class="n">vectorizer</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer"><span class="n">TfidfVectorizer</span></a><span class="p">(</span><span class="n">max_df</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">max_features</span><span class="o">=</span><span class="n">n_features</span><span class="p">,</span>
<span class="n">stop_words</span><span class="o">=</span><span class="s">'english'</span><span class="p">)</span>
<span class="n">tfidf</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">data</span><span class="p">[:</span><span class="n">n_samples</span><span class="p">])</span>
<span class="k">print</span><span class="p">(</span><span class="s">"done in </span><span class="si">%0.3f</span><span class="s">s."</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>
<span class="c"># Fit the NMF model</span>
<span class="k">print</span><span class="p">(</span><span class="s">"Fitting the NMF model with n_samples=</span><span class="si">%d</span><span class="s"> and n_features=</span><span class="si">%d</span><span class="s">..."</span>
<span class="o">%</span> <span class="p">(</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span><span class="p">))</span>
<span class="n">nmf</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF"><span class="n">NMF</span></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="n">n_topics</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">tfidf</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s">"done in </span><span class="si">%0.3f</span><span class="s">s."</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>
<span class="n">feature_names</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">get_feature_names</span><span class="p">()</span>
<span class="k">for</span> <span class="n">topic_idx</span><span class="p">,</span> <span class="n">topic</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">nmf</span><span class="o">.</span><span class="n">components_</span><span class="p">):</span>
<span class="k">print</span><span class="p">(</span><span class="s">"Topic #</span><span class="si">%d</span><span class="s">:"</span> <span class="o">%</span> <span class="n">topic_idx</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s">" "</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">feature_names</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">topic</span><span class="o">.</span><span class="n">argsort</span><span class="p">()[:</span><span class="o">-</span><span class="n">n_top_words</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]]))</span>
<span class="k">print</span><span class="p">()</span>
</pre></div>
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