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<div class="section" id="classification-of-text-documents-using-sparse-features">
<span id="example-document-classification-20newsgroups-py"></span><h1>Classification of text documents using sparse features<a class="headerlink" href="#classification-of-text-documents-using-sparse-features" title="Permalink to this headline">ΒΆ</a></h1>
<p>This is an example showing how the scikit-learn can be used to classify
documents by topics using a bag-of-words approach. This example uses
a scipy.sparse matrix to store the features instead of standard numpy arrays.</p>
<p>The dataset used in this example is the 20 newsgroups dataset which will be
automatically downloaded and then cached.</p>
<p>You can adjust the number of categories by giving there name to the dataset
loader or setting them to None to get the 20 of them.</p>
<p>This example demos various linear classifiers with different training
strategies.</p>
<p>To run this example use:</p>
<div class="highlight-python"><pre>% python examples/document_classification_20newsgroups.py [options]</pre>
</div>
<p>Options are:</p>
<blockquote>
<table class="docutils option-list" frame="void" rules="none">
<col class="option" />
<col class="description" />
<tbody valign="top">
<tr><td class="option-group">
<kbd><span class="option">--report</span></kbd></td>
<td>Print a detailed classification report.</td></tr>
<tr><td class="option-group" colspan="2">
<kbd><span class="option">--confusion-matrix</span></kbd></td>
</tr>
<tr><td> </td><td>Print the confusion matrix.</td></tr>
</tbody>
</table>
</blockquote>
<p><strong>Python source code:</strong> <a class="reference download internal" href="../_downloads/document_classification_20newsgroups.py"><tt class="xref download docutils literal"><span class="pre">document_classification_20newsgroups.py</span></tt></a></p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">print</span> <span class="n">__doc__</span>
<span class="c"># Author: Peter Prettenhofer <peter.prettenhofer@gmail.com></span>
<span class="c"># Olivier Grisel <olivier.grisel@ensta.org></span>
<span class="c"># Mathieu Blondel <mathieu@mblondel.org></span>
<span class="c"># License: Simplified BSD</span>
<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">time</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">from</span> <span class="nn">scikits.learn.datasets</span> <span class="kn">import</span> <span class="n">load_files</span>
<span class="kn">from</span> <span class="nn">scikits.learn.feature_extraction.text.sparse</span> <span class="kn">import</span> <span class="n">Vectorizer</span>
<span class="kn">from</span> <span class="nn">scikits.learn.svm.sparse</span> <span class="kn">import</span> <span class="n">LinearSVC</span>
<span class="kn">from</span> <span class="nn">scikits.learn.linear_model.sparse</span> <span class="kn">import</span> <span class="n">SGDClassifier</span>
<span class="kn">from</span> <span class="nn">scikits.learn</span> <span class="kn">import</span> <span class="n">metrics</span>
<span class="c"># parse commandline arguments</span>
<span class="n">argv</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
<span class="k">if</span> <span class="s">"--report"</span> <span class="ow">in</span> <span class="n">argv</span><span class="p">:</span>
<span class="n">print_report</span> <span class="o">=</span> <span class="bp">True</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">print_report</span> <span class="o">=</span> <span class="bp">False</span>
<span class="k">if</span> <span class="s">"--confusion-matrix"</span> <span class="ow">in</span> <span class="n">argv</span><span class="p">:</span>
<span class="n">print_cm</span> <span class="o">=</span> <span class="bp">True</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">print_cm</span> <span class="o">=</span> <span class="bp">False</span>
<span class="c">################################################################################</span>
<span class="c"># Download the data, if not already on disk</span>
<span class="n">url</span> <span class="o">=</span> <span class="s">"http://people.csail.mit.edu/jrennie/20Newsgroups/20news-18828.tar.gz"</span>
<span class="n">archive_name</span> <span class="o">=</span> <span class="s">"20news-18828.tar.gz"</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">archive_name</span><span class="p">[:</span><span class="o">-</span><span class="mi">7</span><span class="p">]):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">archive_name</span><span class="p">):</span>
<span class="kn">import</span> <span class="nn">urllib</span>
<span class="k">print</span> <span class="s">"Downloading data, please Wait (14MB)..."</span>
<span class="k">print</span> <span class="n">url</span>
<span class="n">opener</span> <span class="o">=</span> <span class="n">urllib</span><span class="o">.</span><span class="n">urlopen</span><span class="p">(</span><span class="n">url</span><span class="p">)</span>
<span class="nb">open</span><span class="p">(</span><span class="n">archive_name</span><span class="p">,</span> <span class="s">'wb'</span><span class="p">)</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">opener</span><span class="o">.</span><span class="n">read</span><span class="p">())</span>
<span class="k">print</span>
<span class="kn">import</span> <span class="nn">tarfile</span>
<span class="k">print</span> <span class="s">"Decompressiong the archive: "</span> <span class="o">+</span> <span class="n">archive_name</span>
<span class="n">tarfile</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">archive_name</span><span class="p">,</span> <span class="s">"r:gz"</span><span class="p">)</span><span class="o">.</span><span class="n">extractall</span><span class="p">()</span>
<span class="k">print</span>
<span class="c">################################################################################</span>
<span class="c"># Load some categories from the training set</span>
<span class="n">categories</span> <span class="o">=</span> <span class="p">[</span>
<span class="s">'alt.atheism'</span><span class="p">,</span>
<span class="s">'talk.religion.misc'</span><span class="p">,</span>
<span class="s">'comp.graphics'</span><span class="p">,</span>
<span class="s">'sci.space'</span><span class="p">,</span>
<span class="p">]</span>
<span class="c"># Uncomment the following to do the analysis on all the categories</span>
<span class="c">#categories = None</span>
<span class="k">print</span> <span class="s">"Loading 20 newsgroups dataset for categories:"</span>
<span class="k">print</span> <span class="n">categories</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">load_files</span><span class="p">(</span><span class="s">'20news-18828'</span><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">shuffle</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">rng</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="k">print</span> <span class="s">"</span><span class="si">%d</span><span class="s"> documents"</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">filenames</span><span class="p">)</span>
<span class="k">print</span> <span class="s">"</span><span class="si">%d</span><span class="s"> categories"</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">target_names</span><span class="p">)</span>
<span class="k">print</span>
<span class="c"># split a training set and a test set</span>
<span class="n">filenames</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">filenames</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">target</span>
<span class="n">n</span> <span class="o">=</span> <span class="n">filenames</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">filenames_train</span><span class="p">,</span> <span class="n">filenames_test</span> <span class="o">=</span> <span class="n">filenames</span><span class="p">[:</span><span class="o">-</span><span class="n">n</span><span class="o">/</span><span class="mi">2</span><span class="p">],</span> <span class="n">filenames</span><span class="p">[</span><span class="o">-</span><span class="n">n</span><span class="o">/</span><span class="mi">2</span><span class="p">:]</span>
<span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">y</span><span class="p">[:</span><span class="o">-</span><span class="n">n</span><span class="o">/</span><span class="mi">2</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="o">-</span><span class="n">n</span><span class="o">/</span><span class="mi">2</span><span class="p">:]</span>
<span class="k">print</span> <span class="s">"Extracting features from the training dataset using a sparse vectorizer"</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">vectorizer</span> <span class="o">=</span> <span class="n">Vectorizer</span><span class="p">()</span>
<span class="n">X_train</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="nb">open</span><span class="p">(</span><span class="n">f</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">()</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">filenames_train</span><span class="p">))</span>
<span class="k">print</span> <span class="s">"done in </span><span class="si">%f</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="k">print</span> <span class="s">"n_samples: </span><span class="si">%d</span><span class="s">, n_features: </span><span class="si">%d</span><span class="s">"</span> <span class="o">%</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span>
<span class="k">print</span>
<span class="k">print</span> <span class="s">"Extracting features from the test dataset using the same vectorizer"</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">transform</span><span class="p">((</span><span class="nb">open</span><span class="p">(</span><span class="n">f</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">()</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">filenames_test</span><span class="p">))</span>
<span class="k">print</span> <span class="s">"done in </span><span class="si">%f</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="k">print</span> <span class="s">"n_samples: </span><span class="si">%d</span><span class="s">, n_features: </span><span class="si">%d</span><span class="s">"</span> <span class="o">%</span> <span class="n">X_test</span><span class="o">.</span><span class="n">shape</span>
<span class="k">print</span>
<span class="c">################################################################################</span>
<span class="c"># Benchmark classifiers</span>
<span class="k">def</span> <span class="nf">benchmark</span><span class="p">(</span><span class="n">clf</span><span class="p">):</span>
<span class="k">print</span> <span class="mi">80</span> <span class="o">*</span> <span class="s">'_'</span>
<span class="k">print</span> <span class="s">"Training: "</span>
<span class="k">print</span> <span class="n">clf</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">train_time</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span>
<span class="k">print</span> <span class="s">"train time: </span><span class="si">%0.3f</span><span class="s">s"</span> <span class="o">%</span> <span class="n">train_time</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="n">test_time</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span>
<span class="k">print</span> <span class="s">"test time: </span><span class="si">%0.3f</span><span class="s">s"</span> <span class="o">%</span> <span class="n">test_time</span>
<span class="n">score</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">f1_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span>
<span class="k">print</span> <span class="s">"f1-score: </span><span class="si">%0.3f</span><span class="s">"</span> <span class="o">%</span> <span class="n">score</span>
<span class="n">nnz</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">coef_</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">print</span> <span class="s">"non-zero coef: </span><span class="si">%d</span><span class="s">"</span> <span class="o">%</span> <span class="n">nnz</span>
<span class="k">print</span>
<span class="k">if</span> <span class="n">print_report</span><span class="p">:</span>
<span class="k">print</span> <span class="s">"classification report:"</span>
<span class="k">print</span> <span class="n">metrics</span><span class="o">.</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred</span><span class="p">,</span>
<span class="n">class_names</span><span class="o">=</span><span class="n">categories</span><span class="p">)</span>
<span class="k">if</span> <span class="n">print_cm</span><span class="p">:</span>
<span class="k">print</span> <span class="s">"confusion matrix:"</span>
<span class="k">print</span> <span class="n">metrics</span><span class="o">.</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span>
<span class="k">print</span>
<span class="k">return</span> <span class="n">score</span><span class="p">,</span> <span class="n">train_time</span><span class="p">,</span> <span class="n">test_time</span>
<span class="k">for</span> <span class="n">penalty</span> <span class="ow">in</span> <span class="p">[</span><span class="s">"l2"</span><span class="p">,</span> <span class="s">"l1"</span><span class="p">]:</span>
<span class="k">print</span> <span class="mi">80</span><span class="o">*</span><span class="s">'='</span>
<span class="k">print</span> <span class="s">"</span><span class="si">%s</span><span class="s"> penalty"</span> <span class="o">%</span> <span class="n">penalty</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span>
<span class="c"># Train Liblinear model</span>
<span class="n">liblinear_results</span> <span class="o">=</span> <span class="n">benchmark</span><span class="p">(</span><span class="n">LinearSVC</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s">'l2'</span><span class="p">,</span> <span class="n">penalty</span><span class="o">=</span><span class="n">penalty</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
<span class="n">dual</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">))</span>
<span class="c"># Train SGD model</span>
<span class="n">sgd_results</span> <span class="o">=</span> <span class="n">benchmark</span><span class="p">(</span><span class="n">SGDClassifier</span><span class="p">(</span><span class="n">alpha</span><span class="o">=.</span><span class="mo">0001</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
<span class="n">penalty</span><span class="o">=</span><span class="n">penalty</span><span class="p">))</span>
<span class="c"># Train SGD with Elastic Net penalty</span>
<span class="k">print</span> <span class="mi">80</span><span class="o">*</span><span class="s">'='</span>
<span class="k">print</span> <span class="s">"Elastic-Net penalty"</span>
<span class="n">sgd_results</span> <span class="o">=</span> <span class="n">benchmark</span><span class="p">(</span><span class="n">SGDClassifier</span><span class="p">(</span><span class="n">alpha</span><span class="o">=.</span><span class="mo">0001</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
<span class="n">penalty</span><span class="o">=</span><span class="s">"elasticnet"</span><span class="p">))</span>
</pre></div>
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i586
>
media
>
contrib-backports
>
by-pkgid
>
e578866d55cd81fdb23827cdf3cec911
>
files
>
633
