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<!DOCTYPE html>
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<li><a class="reference internal" href="#">Biclustering documents with the Spectral Co-clustering algorithm</a></li>
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<p class="admonition-title">Note</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-bicluster-plot-bicluster-newsgroups-py"><span class="std std-ref">here</span></a> to download the full example code or to run this example in your browser via Binder</p>
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<div class="sphx-glr-example-title section" id="biclustering-documents-with-the-spectral-co-clustering-algorithm">
<span id="sphx-glr-auto-examples-bicluster-plot-bicluster-newsgroups-py"></span><h1>Biclustering documents with the Spectral Co-clustering algorithm<a class="headerlink" href="#biclustering-documents-with-the-spectral-co-clustering-algorithm" title="Permalink to this headline">¶</a></h1>
<p>This example demonstrates the Spectral Co-clustering algorithm on the
twenty newsgroups dataset. The ‘comp.os.ms-windows.misc’ category is
excluded because it contains many posts containing nothing but data.</p>
<p>The TF-IDF vectorized posts form a word frequency matrix, which is
then biclustered using Dhillon’s Spectral Co-Clustering algorithm. The
resulting document-word biclusters indicate subsets words used more
often in those subsets documents.</p>
<p>For a few of the best biclusters, its most common document categories
and its ten most important words get printed. The best biclusters are
determined by their normalized cut. The best words are determined by
comparing their sums inside and outside the bicluster.</p>
<p>For comparison, the documents are also clustered using
MiniBatchKMeans. The document clusters derived from the biclusters
achieve a better V-measure than clusters found by MiniBatchKMeans.</p>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Vectorizing...
Coclustering...
Done in 2.75s. V-measure: 0.4387
MiniBatchKMeans...
Done in 5.69s. V-measure: 0.3344
Best biclusters:
----------------
bicluster 0 : 1829 documents, 2524 words
categories : 22% comp.sys.ibm.pc.hardware, 19% comp.sys.mac.hardware, 18% comp.graphics
words : card, pc, ram, drive, bus, mac, motherboard, port, windows, floppy
bicluster 1 : 2391 documents, 3275 words
categories : 18% rec.motorcycles, 17% rec.autos, 15% sci.electronics
words : bike, engine, car, dod, bmw, honda, oil, motorcycle, behanna, ysu
bicluster 2 : 1887 documents, 4232 words
categories : 23% talk.politics.guns, 19% talk.politics.misc, 13% sci.med
words : gun, guns, firearms, geb, drugs, banks, dyer, amendment, clinton, cdt
bicluster 3 : 1146 documents, 3263 words
categories : 29% talk.politics.mideast, 26% soc.religion.christian, 25% alt.atheism
words : god, jesus, christians, atheists, kent, sin, morality, belief, resurrection, marriage
bicluster 4 : 1732 documents, 3967 words
categories : 26% sci.crypt, 23% sci.space, 17% sci.med
words : clipper, encryption, key, escrow, nsa, crypto, keys, intercon, secure, wiretap
</pre></div>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/collections.html#collections.defaultdict" title="View documentation for collections.defaultdict"><span class="n">defaultdict</span></a>
<span class="kn">import</span> <span class="nn">operator</span>
<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="View documentation for time.time"><span class="n">time</span></a>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.cluster.SpectralCoclustering.html#sklearn.cluster.SpectralCoclustering" title="View documentation for sklearn.cluster.SpectralCoclustering"><span class="n">SpectralCoclustering</span></a>
<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.cluster.MiniBatchKMeans.html#sklearn.cluster.MiniBatchKMeans" title="View documentation for sklearn.cluster.MiniBatchKMeans"><span class="n">MiniBatchKMeans</span></a>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.datasets.fetch_20newsgroups.html#sklearn.datasets.fetch_20newsgroups" title="View documentation for sklearn.datasets.fetch_20newsgroups"><span class="n">fetch_20newsgroups</span></a>
<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" title="View documentation for sklearn.feature_extraction.text.TfidfVectorizer"><span class="n">TfidfVectorizer</span></a>
<span class="kn">from</span> <span class="nn">sklearn.metrics.cluster</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.metrics.v_measure_score.html#sklearn.metrics.v_measure_score" title="View documentation for sklearn.metrics.v_measure_score"><span class="n">v_measure_score</span></a>
<span class="nb">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">number_normalizer</span><span class="p">(</span><span class="n">tokens</span><span class="p">):</span>
<span class="sd">""" Map all numeric tokens to a placeholder.</span>
<span class="sd"> For many applications, tokens that begin with a number are not directly</span>
<span class="sd"> useful, but the fact that such a token exists can be relevant. By applying</span>
<span class="sd"> this form of dimensionality reduction, some methods may perform better.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="p">(</span><span class="s2">"#NUMBER"</span> <span class="k">if</span> <span class="n">token</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">isdigit</span><span class="p">()</span> <span class="k">else</span> <span class="n">token</span> <span class="k">for</span> <span class="n">token</span> <span class="ow">in</span> <span class="n">tokens</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">NumberNormalizingVectorizer</span><span class="p">(</span><a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer" title="View documentation for sklearn.feature_extraction.text.TfidfVectorizer"><span class="n">TfidfVectorizer</span></a><span class="p">):</span>
<span class="k">def</span> <span class="nf">build_tokenizer</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">tokenize</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">build_tokenizer</span><span class="p">()</span>
<span class="k">return</span> <span class="k">lambda</span> <span class="n">doc</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="n">number_normalizer</span><span class="p">(</span><span class="n">tokenize</span><span class="p">(</span><span class="n">doc</span><span class="p">)))</span>
<span class="c1"># exclude 'comp.os.ms-windows.misc'</span>
<span class="n">categories</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'alt.atheism'</span><span class="p">,</span> <span class="s1">'comp.graphics'</span><span class="p">,</span>
<span class="s1">'comp.sys.ibm.pc.hardware'</span><span class="p">,</span> <span class="s1">'comp.sys.mac.hardware'</span><span class="p">,</span>
<span class="s1">'comp.windows.x'</span><span class="p">,</span> <span class="s1">'misc.forsale'</span><span class="p">,</span> <span class="s1">'rec.autos'</span><span class="p">,</span>
<span class="s1">'rec.motorcycles'</span><span class="p">,</span> <span class="s1">'rec.sport.baseball'</span><span class="p">,</span>
<span class="s1">'rec.sport.hockey'</span><span class="p">,</span> <span class="s1">'sci.crypt'</span><span class="p">,</span> <span class="s1">'sci.electronics'</span><span class="p">,</span>
<span class="s1">'sci.med'</span><span class="p">,</span> <span class="s1">'sci.space'</span><span class="p">,</span> <span class="s1">'soc.religion.christian'</span><span class="p">,</span>
<span class="s1">'talk.politics.guns'</span><span class="p">,</span> <span class="s1">'talk.politics.mideast'</span><span class="p">,</span>
<span class="s1">'talk.politics.misc'</span><span class="p">,</span> <span class="s1">'talk.religion.misc'</span><span class="p">]</span>
<span class="n">newsgroups</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.fetch_20newsgroups.html#sklearn.datasets.fetch_20newsgroups" title="View documentation for sklearn.datasets.fetch_20newsgroups"><span class="n">fetch_20newsgroups</span></a><span class="p">(</span><span class="n">categories</span><span class="o">=</span><span class="n">categories</span><span class="p">)</span>
<span class="n">y_true</span> <span class="o">=</span> <span class="n">newsgroups</span><span class="o">.</span><span class="n">target</span>
<span class="n">vectorizer</span> <span class="o">=</span> <span class="n">NumberNormalizingVectorizer</span><span class="p">(</span><span class="n">stop_words</span><span class="o">=</span><span class="s1">'english'</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="n">cocluster</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.cluster.SpectralCoclustering.html#sklearn.cluster.SpectralCoclustering" title="View documentation for sklearn.cluster.SpectralCoclustering"><span class="n">SpectralCoclustering</span></a><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">categories</span><span class="p">),</span>
<span class="n">svd_method</span><span class="o">=</span><span class="s1">'arpack'</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">kmeans</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.cluster.MiniBatchKMeans.html#sklearn.cluster.MiniBatchKMeans" title="View documentation for sklearn.cluster.MiniBatchKMeans"><span class="n">MiniBatchKMeans</span></a><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">categories</span><span class="p">),</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">20000</span><span class="p">,</span>
<span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Vectorizing..."</span><span class="p">)</span>
<span class="n">X</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">newsgroups</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Coclustering..."</span><span class="p">)</span>
<span class="n">start_time</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="View documentation for time.time"><span class="n">time</span></a><span class="p">()</span>
<span class="n">cocluster</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">y_cocluster</span> <span class="o">=</span> <span class="n">cocluster</span><span class="o">.</span><span class="n">row_labels_</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Done in </span><span class="si">{:.2f}</span><span class="s2">s. V-measure: </span><span class="si">{:.4f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<a href="https://docs.python.org/3/library/time.html#time.time" title="View documentation for time.time"><span class="n">time</span></a><span class="p">()</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">,</span>
<a href="../../modules/generated/sklearn.metrics.v_measure_score.html#sklearn.metrics.v_measure_score" title="View documentation for sklearn.metrics.v_measure_score"><span class="n">v_measure_score</span></a><span class="p">(</span><span class="n">y_cocluster</span><span class="p">,</span> <span class="n">y_true</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"MiniBatchKMeans..."</span><span class="p">)</span>
<span class="n">start_time</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="View documentation for time.time"><span class="n">time</span></a><span class="p">()</span>
<span class="n">y_kmeans</span> <span class="o">=</span> <span class="n">kmeans</span><span class="o">.</span><span class="n">fit_predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Done in </span><span class="si">{:.2f}</span><span class="s2">s. V-measure: </span><span class="si">{:.4f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<a href="https://docs.python.org/3/library/time.html#time.time" title="View documentation for time.time"><span class="n">time</span></a><span class="p">()</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">,</span>
<a href="../../modules/generated/sklearn.metrics.v_measure_score.html#sklearn.metrics.v_measure_score" title="View documentation for sklearn.metrics.v_measure_score"><span class="n">v_measure_score</span></a><span class="p">(</span><span class="n">y_kmeans</span><span class="p">,</span> <span class="n">y_true</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="n">document_names</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">newsgroups</span><span class="o">.</span><span class="n">target_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">newsgroups</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">bicluster_ncut</span><span class="p">(</span><span class="n">i</span><span class="p">):</span>
<span class="n">rows</span><span class="p">,</span> <span class="n">cols</span> <span class="o">=</span> <span class="n">cocluster</span><span class="o">.</span><span class="n">get_indices</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.any.html#numpy.any" title="View documentation for numpy.any"><span class="n">np</span><span class="o">.</span><span class="n">any</span></a><span class="p">(</span><span class="n">rows</span><span class="p">)</span> <span class="ow">and</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.any.html#numpy.any" title="View documentation for numpy.any"><span class="n">np</span><span class="o">.</span><span class="n">any</span></a><span class="p">(</span><span class="n">cols</span><span class="p">)):</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="k">return</span> <span class="n">sys</span><span class="o">.</span><span class="n">float_info</span><span class="o">.</span><span class="n">max</span>
<span class="n">row_complement</span> <span class="o">=</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.nonzero.html#numpy.nonzero" title="View documentation for numpy.nonzero"><span class="n">np</span><span class="o">.</span><span class="n">nonzero</span></a><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.logical_not.html#numpy.logical_not" title="View documentation for numpy.logical_not"><span class="n">np</span><span class="o">.</span><span class="n">logical_not</span></a><span class="p">(</span><span class="n">cocluster</span><span class="o">.</span><span class="n">rows_</span><span class="p">[</span><span class="n">i</span><span class="p">]))[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">col_complement</span> <span class="o">=</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.nonzero.html#numpy.nonzero" title="View documentation for numpy.nonzero"><span class="n">np</span><span class="o">.</span><span class="n">nonzero</span></a><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.logical_not.html#numpy.logical_not" title="View documentation for numpy.logical_not"><span class="n">np</span><span class="o">.</span><span class="n">logical_not</span></a><span class="p">(</span><span class="n">cocluster</span><span class="o">.</span><span class="n">columns_</span><span class="p">[</span><span class="n">i</span><span class="p">]))[</span><span class="mi">0</span><span class="p">]</span>
<span class="c1"># Note: the following is identical to X[rows[:, np.newaxis],</span>
<span class="c1"># cols].sum() but much faster in scipy <= 0.16</span>
<span class="n">weight</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">rows</span><span class="p">][:,</span> <span class="n">cols</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">cut</span> <span class="o">=</span> <span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">row_complement</span><span class="p">][:,</span> <span class="n">cols</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">+</span>
<span class="n">X</span><span class="p">[</span><span class="n">rows</span><span class="p">][:,</span> <span class="n">col_complement</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span>
<span class="k">return</span> <span class="n">cut</span> <span class="o">/</span> <span class="n">weight</span>
<span class="k">def</span> <span class="nf">most_common</span><span class="p">(</span><span class="n">d</span><span class="p">):</span>
<span class="sd">"""Items of a defaultdict(int) with the highest values.</span>
<span class="sd"> Like Counter.most_common in Python >=2.7.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">items</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><a href="https://docs.python.org/3/library/operator.html#operator.itemgetter" title="View documentation for operator.itemgetter"><span class="n">operator</span><span class="o">.</span><span class="n">itemgetter</span></a><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">bicluster_ncuts</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">bicluster_ncut</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="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">newsgroups</span><span class="o">.</span><span class="n">target_names</span><span class="p">)))</span>
<span class="n">best_idx</span> <span class="o">=</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.argsort.html#numpy.argsort" title="View documentation for numpy.argsort"><span class="n">np</span><span class="o">.</span><span class="n">argsort</span></a><span class="p">(</span><span class="n">bicluster_ncuts</span><span class="p">)[:</span><span class="mi">5</span><span class="p">]</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Best biclusters:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"----------------"</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">best_idx</span><span class="p">):</span>
<span class="n">n_rows</span><span class="p">,</span> <span class="n">n_cols</span> <span class="o">=</span> <span class="n">cocluster</span><span class="o">.</span><span class="n">get_shape</span><span class="p">(</span><span class="n">cluster</span><span class="p">)</span>
<span class="n">cluster_docs</span><span class="p">,</span> <span class="n">cluster_words</span> <span class="o">=</span> <span class="n">cocluster</span><span class="o">.</span><span class="n">get_indices</span><span class="p">(</span><span class="n">cluster</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">len</span><span class="p">(</span><span class="n">cluster_docs</span><span class="p">)</span> <span class="ow">or</span> <span class="ow">not</span> <span class="nb">len</span><span class="p">(</span><span class="n">cluster_words</span><span class="p">):</span>
<span class="k">continue</span>
<span class="c1"># categories</span>
<span class="n">counter</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/collections.html#collections.defaultdict" title="View documentation for collections.defaultdict"><span class="n">defaultdict</span></a><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">cluster_docs</span><span class="p">:</span>
<span class="n">counter</span><span class="p">[</span><span class="n">document_names</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">cat_string</span> <span class="o">=</span> <span class="s2">", "</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s2">"</span><span class="si">{:.0f}</span><span class="s2">% </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="o">/</span> <span class="n">n_rows</span> <span class="o">*</span> <span class="mi">100</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">most_common</span><span class="p">(</span><span class="n">counter</span><span class="p">)[:</span><span class="mi">3</span><span class="p">])</span>
<span class="c1"># words</span>
<span class="n">out_of_cluster_docs</span> <span class="o">=</span> <span class="n">cocluster</span><span class="o">.</span><span class="n">row_labels_</span> <span class="o">!=</span> <span class="n">cluster</span>
<span class="n">out_of_cluster_docs</span> <span class="o">=</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html#numpy.where" title="View documentation for numpy.where"><span class="n">np</span><span class="o">.</span><span class="n">where</span></a><span class="p">(</span><span class="n">out_of_cluster_docs</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">word_col</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="n">cluster_words</span><span class="p">]</span>
<span class="n">word_scores</span> <span class="o">=</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.array.html#numpy.array" title="View documentation for numpy.array"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">(</span><span class="n">word_col</span><span class="p">[</span><span class="n">cluster_docs</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">-</span>
<span class="n">word_col</span><span class="p">[</span><span class="n">out_of_cluster_docs</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
<span class="n">word_scores</span> <span class="o">=</span> <span class="n">word_scores</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="n">important_words</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">feature_names</span><span class="p">[</span><span class="n">cluster_words</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">word_scores</span><span class="o">.</span><span class="n">argsort</span><span class="p">()[:</span><span class="o">-</span><span class="mi">11</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"bicluster </span><span class="si">{}</span><span class="s2"> : </span><span class="si">{}</span><span class="s2"> documents, </span><span class="si">{}</span><span class="s2"> words"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">idx</span><span class="p">,</span> <span class="n">n_rows</span><span class="p">,</span> <span class="n">n_cols</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"categories : </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">cat_string</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"words : </span><span class="si">{}</span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">', '</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">important_words</span><span class="p">)))</span>
</pre></div>
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hideNavBar()
} else {
showNavBar()
}
prevScrollpos = lastScrollTop;
};
/*** high performance scroll event listener***/
var raf = window.requestAnimationFrame ||
window.webkitRequestAnimationFrame ||
window.mozRequestAnimationFrame ||
window.msRequestAnimationFrame ||
window.oRequestAnimationFrame;
var lastScrollTop = $window.scrollTop();
if (raf) {
loop();
}
function loop() {
var scrollTop = $window.scrollTop();
if (lastScrollTop === scrollTop) {
raf(loop);
return;
} else {
lastScrollTop = scrollTop;
hideOnScroll(lastScrollTop);
raf(loop);
}
}
})();
});
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