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<h3>Contents</h3>
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<li><a class="reference internal" href="#">Sample pipeline for text feature extraction and evaluation</a></li>
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<div class="section" id="sample-pipeline-for-text-feature-extraction-and-evaluation">
<span id="example-grid-search-text-feature-extraction-py"></span><h1>Sample pipeline for text feature extraction and evaluation<a class="headerlink" href="#sample-pipeline-for-text-feature-extraction-and-evaluation" title="Permalink to this headline">ΒΆ</a></h1>
<p>The dataset used in this example is the 20 newsgroups dataset which will be
automatically downloaded and then cached and reused for the document
classification example.</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>Here is a sample output of a run on a quad-core machine:</p>
<div class="highlight-python"><pre>Loading 20 newsgroups dataset for categories:
['alt.atheism', 'talk.religion.misc']
1427 documents
2 categories
Performing grid search...
pipeline: ['vect', 'tfidf', 'clf']
parameters:
{'clf__alpha': (1.0000000000000001e-05, 9.9999999999999995e-07),
'clf__n_iter': (10, 50, 80),
'clf__penalty': ('l2', 'elasticnet'),
'tfidf__use_idf': (True, False),
'vect__analyzer__max_n': (1, 2),
'vect__max_df': (0.5, 0.75, 1.0),
'vect__max_features': (None, 5000, 10000, 50000)}
done in 1737.030s
Best score: 0.940
Best parameters set:
clf__alpha: 9.9999999999999995e-07
clf__n_iter: 50
clf__penalty: 'elasticnet'
tfidf__use_idf: True
vect__analyzer__max_n: 2
vect__max_df: 0.75
vect__max_features: 50000</pre>
</div>
<p><strong>Python source code:</strong> <a class="reference download internal" href="../_downloads/grid_search_text_feature_extraction.py"><tt class="xref download docutils literal"><span class="pre">grid_search_text_feature_extraction.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: Olivier Grisel <olivier.grisel@ensta.org></span>
<span class="c"># Peter Prettenhofer <peter.prettenhofer@gmail.com></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">pprint</span> <span class="kn">import</span> <span class="n">pprint</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">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">CountVectorizer</span>
<span class="kn">from</span> <span class="nn">scikits.learn.feature_extraction.text.sparse</span> <span class="kn">import</span> <span class="n">TfidfTransformer</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.grid_search</span> <span class="kn">import</span> <span class="n">GridSearchCV</span>
<span class="kn">from</span> <span class="nn">scikits.learn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</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="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="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">################################################################################</span>
<span class="c"># define a pipeline combining a text feature extractor with a simple</span>
<span class="c"># classifier</span>
<span class="n">pipeline</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">([</span>
<span class="p">(</span><span class="s">'vect'</span><span class="p">,</span> <span class="n">CountVectorizer</span><span class="p">()),</span>
<span class="p">(</span><span class="s">'tfidf'</span><span class="p">,</span> <span class="n">TfidfTransformer</span><span class="p">()),</span>
<span class="p">(</span><span class="s">'clf'</span><span class="p">,</span> <span class="n">SGDClassifier</span><span class="p">()),</span>
<span class="p">])</span>
<span class="n">parameters</span> <span class="o">=</span> <span class="p">{</span>
<span class="c"># uncommenting more parameters will give better exploring power but will</span>
<span class="c"># increase processing time in a combinatorial way</span>
<span class="s">'vect__max_df'</span><span class="p">:</span> <span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">),</span>
<span class="c"># 'vect__max_features': (None, 5000, 10000, 50000),</span>
<span class="s">'vect__analyzer__max_n'</span><span class="p">:</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="c"># words or bigrams</span>
<span class="c"># 'tfidf__use_idf': (True, False),</span>
<span class="s">'clf__alpha'</span><span class="p">:</span> <span class="p">(</span><span class="mf">0.00001</span><span class="p">,</span> <span class="mf">0.000001</span><span class="p">),</span>
<span class="s">'clf__penalty'</span><span class="p">:</span> <span class="p">(</span><span class="s">'l2'</span><span class="p">,</span> <span class="s">'elasticnet'</span><span class="p">),</span>
<span class="c"># 'clf__n_iter': (10, 50, 80),</span>
<span class="p">}</span>
<span class="c"># find the best parameters for both the feature extraction and the</span>
<span class="c"># classifier</span>
<span class="n">grid_search</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">pipeline</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
<span class="c"># cross-validation doesn't work if the length of the data is not known,</span>
<span class="c"># hence use lists instead of iterators</span>
<span class="n">text_docs</span> <span class="o">=</span> <span class="p">[</span><span class="nb">file</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">data</span><span class="o">.</span><span class="n">filenames</span><span class="p">]</span>
<span class="k">print</span> <span class="s">"Performing grid search..."</span>
<span class="k">print</span> <span class="s">"pipeline:"</span><span class="p">,</span> <span class="p">[</span><span class="n">name</span> <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">steps</span><span class="p">]</span>
<span class="k">print</span> <span class="s">"parameters:"</span>
<span class="n">pprint</span><span class="p">(</span><span class="n">parameters</span><span class="p">)</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">grid_search</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">text_docs</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
<span class="k">print</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="k">print</span>
<span class="k">print</span> <span class="s">"Best score: </span><span class="si">%0.3f</span><span class="s">"</span> <span class="o">%</span> <span class="n">grid_search</span><span class="o">.</span><span class="n">best_score</span>
<span class="k">print</span> <span class="s">"Best parameters set:"</span>
<span class="n">best_parameters</span> <span class="o">=</span> <span class="n">grid_search</span><span class="o">.</span><span class="n">best_estimator</span><span class="o">.</span><span class="n">_get_params</span><span class="p">()</span>
<span class="k">for</span> <span class="n">param_name</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">parameters</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
<span class="k">print</span> <span class="s">"</span><span class="se">\t</span><span class="si">%s</span><span class="s">: </span><span class="si">%r</span><span class="s">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">param_name</span><span class="p">,</span> <span class="n">best_parameters</span><span class="p">[</span><span class="n">param_name</span><span class="p">])</span>
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