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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="first admonition-title">Note</p>
<p class="last">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</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"><div class="highlight"><pre><span></span>Vectorizing...
Coclustering...
Done in 6.16s. V-measure: 0.4435
MiniBatchKMeans...
Done in 10.52s. V-measure: 0.3344
Best biclusters:
----------------
bicluster 0 : 1957 documents, 4363 words
categories : 23% talk.politics.guns, 18% talk.politics.misc, 17% sci.med
words : gun, guns, geb, banks, gordon, clinton, pitt, cdt, surrender, veal
bicluster 1 : 1263 documents, 3551 words
categories : 27% soc.religion.christian, 25% talk.politics.mideast, 24% alt.atheism
words : god, jesus, christians, sin, objective, kent, belief, christ, faith, moral
bicluster 2 : 2212 documents, 2774 words
categories : 18% comp.sys.mac.hardware, 17% comp.sys.ibm.pc.hardware, 15% comp.graphics
words : voltage, board, dsp, stereo, receiver, packages, shipping, circuit, package, compression
bicluster 3 : 1774 documents, 2629 words
categories : 27% rec.motorcycles, 23% rec.autos, 13% misc.forsale
words : bike, car, dod, engine, motorcycle, ride, honda, bikes, helmet, bmw
bicluster 4 : 200 documents, 1167 words
categories : 81% talk.politics.mideast, 10% alt.atheism, 8% soc.religion.christian
words : turkish, armenia, armenian, armenians, turks, petch, sera, zuma, argic, gvg47
</pre></div>
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<div class="highlight-default"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">print_function</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="k">import</span> <span class="n">defaultdict</span>
<span class="kn">import</span> <span class="nn">operator</span>
<span class="kn">from</span> <span class="nn">time</span> <span class="k">import</span> <span class="n">time</span>
<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.bicluster</span> <span class="k">import</span> <span class="n">SpectralCoclustering</span>
<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="k">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.externals.six</span> <span class="k">import</span> <span class="n">iteritems</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets.twenty_newsgroups</span> <span class="k">import</span> <a href="../../modules/generated/sklearn.datasets.fetch_20newsgroups.html#sklearn.datasets.fetch_20newsgroups" title="View documentation for sklearn.datasets.twenty_newsgroups.fetch_20newsgroups"><span class="n">fetch_20newsgroups</span></a>
<span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="k">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="k">import</span> <a href="../../modules/generated/sklearn.metrics.v_measure_score.html#sklearn.metrics.v_measure_score" title="View documentation for sklearn.metrics.cluster.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="n">NumberNormalizingVectorizer</span><span class="p">,</span> <span class="bp">self</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.twenty_newsgroups.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> <span class="n">SpectralCoclustering</span><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> <span class="n">time</span><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>
<span class="n">time</span><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.cluster.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> <span class="n">time</span><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>
<span class="n">time</span><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.cluster.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">iteritems</span><span class="p">(</span><span class="n">d</span><span class="p">),</span> <span class="n">key</span><span class="o">=</span><span class="n">operator</span><span class="o">.</span><span class="n">itemgetter</span><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> <span class="n">defaultdict</span><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>
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