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<div class="section" id="sample-pipeline-for-text-feature-extraction-and-evaluation">
<span id="sphx-glr-auto-examples-model-selection-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">¶</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 their names 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-default"><div class="highlight"><pre><span></span><span class="n">Loading</span> <span class="mi">20</span> <span class="n">newsgroups</span> <span class="n">dataset</span> <span class="k">for</span> <span class="n">categories</span><span class="p">:</span>
<span class="p">[</span><span class="s1">'alt.atheism'</span><span class="p">,</span> <span class="s1">'talk.religion.misc'</span><span class="p">]</span>
<span class="mi">1427</span> <span class="n">documents</span>
<span class="mi">2</span> <span class="n">categories</span>
<span class="n">Performing</span> <span class="n">grid</span> <span class="n">search</span><span class="o">...</span>
<span class="n">pipeline</span><span class="p">:</span> <span class="p">[</span><span class="s1">'vect'</span><span class="p">,</span> <span class="s1">'tfidf'</span><span class="p">,</span> <span class="s1">'clf'</span><span class="p">]</span>
<span class="n">parameters</span><span class="p">:</span>
<span class="p">{</span><span class="s1">'clf__alpha'</span><span class="p">:</span> <span class="p">(</span><span class="mf">1.0000000000000001e-05</span><span class="p">,</span> <span class="mf">9.9999999999999995e-07</span><span class="p">),</span>
<span class="s1">'clf__n_iter'</span><span class="p">:</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">80</span><span class="p">),</span>
<span class="s1">'clf__penalty'</span><span class="p">:</span> <span class="p">(</span><span class="s1">'l2'</span><span class="p">,</span> <span class="s1">'elasticnet'</span><span class="p">),</span>
<span class="s1">'tfidf__use_idf'</span><span class="p">:</span> <span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">),</span>
<span class="s1">'vect__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="s1">'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="s1">'vect__max_features'</span><span class="p">:</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="mi">5000</span><span class="p">,</span> <span class="mi">10000</span><span class="p">,</span> <span class="mi">50000</span><span class="p">)}</span>
<span class="n">done</span> <span class="ow">in</span> <span class="mf">1737.030</span><span class="n">s</span>
<span class="n">Best</span> <span class="n">score</span><span class="p">:</span> <span class="mf">0.940</span>
<span class="n">Best</span> <span class="n">parameters</span> <span class="nb">set</span><span class="p">:</span>
<span class="n">clf__alpha</span><span class="p">:</span> <span class="mf">9.9999999999999995e-07</span>
<span class="n">clf__n_iter</span><span class="p">:</span> <span class="mi">50</span>
<span class="n">clf__penalty</span><span class="p">:</span> <span class="s1">'elasticnet'</span>
<span class="n">tfidf__use_idf</span><span class="p">:</span> <span class="kc">True</span>
<span class="n">vect__max_n</span><span class="p">:</span> <span class="mi">2</span>
<span class="n">vect__max_df</span><span class="p">:</span> <span class="mf">0.75</span>
<span class="n">vect__max_features</span><span class="p">:</span> <span class="mi">50000</span>
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># Author: Olivier Grisel <[email protected]></span>
<span class="c1"># Peter Prettenhofer <[email protected]></span>
<span class="c1"># Mathieu Blondel <[email protected]></span>
<span class="c1"># 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">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">logging</span>
<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.CountVectorizer.html#sklearn.feature_extraction.text.CountVectorizer" title="View documentation for sklearn.feature_extraction.text.CountVectorizer"><span class="n">CountVectorizer</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.TfidfTransformer.html#sklearn.feature_extraction.text.TfidfTransformer" title="View documentation for sklearn.feature_extraction.text.TfidfTransformer"><span class="n">TfidfTransformer</span></a>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier" title="View documentation for sklearn.linear_model.SGDClassifier"><span class="n">SGDClassifier</span></a>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="View documentation for sklearn.model_selection.GridSearchCV"><span class="n">GridSearchCV</span></a>
<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="View documentation for sklearn.pipeline.Pipeline"><span class="n">Pipeline</span></a>
<span class="k">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>
<span class="c1"># Display progress logs on stdout</span>
<span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">INFO</span><span class="p">,</span>
<span class="n">format</span><span class="o">=</span><span class="s1">'</span><span class="si">%(asctime)s</span><span class="s1"> </span><span class="si">%(levelname)s</span><span class="s1"> </span><span class="si">%(message)s</span><span class="s1">'</span><span class="p">)</span>
<span class="c1"># #############################################################################</span>
<span class="c1"># Load some categories from the training set</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">'talk.religion.misc'</span><span class="p">,</span>
<span class="p">]</span>
<span class="c1"># Uncomment the following to do the analysis on all the categories</span>
<span class="c1">#categories = None</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Loading 20 newsgroups dataset for categories:"</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">categories</span><span class="p">)</span>
<span class="n">data</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">subset</span><span class="o">=</span><span class="s1">'train'</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="p">(</span><span class="s2">"</span><span class="si">%d</span><span class="s2"> 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="p">(</span><span class="s2">"</span><span class="si">%d</span><span class="s2"> 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="p">()</span>
<span class="c1"># #############################################################################</span>
<span class="c1"># Define a pipeline combining a text feature extractor with a simple</span>
<span class="c1"># classifier</span>
<span class="n">pipeline</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="View documentation for sklearn.pipeline.Pipeline"><span class="n">Pipeline</span></a><span class="p">([</span>
<span class="p">(</span><span class="s1">'vect'</span><span class="p">,</span> <a href="../../modules/generated/sklearn.feature_extraction.text.CountVectorizer.html#sklearn.feature_extraction.text.CountVectorizer" title="View documentation for sklearn.feature_extraction.text.CountVectorizer"><span class="n">CountVectorizer</span></a><span class="p">()),</span>
<span class="p">(</span><span class="s1">'tfidf'</span><span class="p">,</span> <a href="../../modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html#sklearn.feature_extraction.text.TfidfTransformer" title="View documentation for sklearn.feature_extraction.text.TfidfTransformer"><span class="n">TfidfTransformer</span></a><span class="p">()),</span>
<span class="p">(</span><span class="s1">'clf'</span><span class="p">,</span> <a href="../../modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier" title="View documentation for sklearn.linear_model.SGDClassifier"><span class="n">SGDClassifier</span></a><span class="p">()),</span>
<span class="p">])</span>
<span class="c1"># uncommenting more parameters will give better exploring power but will</span>
<span class="c1"># increase processing time in a combinatorial way</span>
<span class="n">parameters</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">'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="c1">#'vect__max_features': (None, 5000, 10000, 50000),</span>
<span class="s1">'vect__ngram_range'</span><span class="p">:</span> <span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</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="c1"># unigrams or bigrams</span>
<span class="c1">#'tfidf__use_idf': (True, False),</span>
<span class="c1">#'tfidf__norm': ('l1', 'l2'),</span>
<span class="s1">'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="s1">'clf__penalty'</span><span class="p">:</span> <span class="p">(</span><span class="s1">'l2'</span><span class="p">,</span> <span class="s1">'elasticnet'</span><span class="p">),</span>
<span class="c1">#'clf__n_iter': (10, 50, 80),</span>
<span class="p">}</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">"__main__"</span><span class="p">:</span>
<span class="c1"># multiprocessing requires the fork to happen in a __main__ protected</span>
<span class="c1"># block</span>
<span class="c1"># find the best parameters for both the feature extraction and the</span>
<span class="c1"># classifier</span>
<span class="n">grid_search</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="View documentation for sklearn.model_selection.GridSearchCV"><span class="n">GridSearchCV</span></a><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="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Performing grid search..."</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"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="p">(</span><span class="s2">"parameters:"</span><span class="p">)</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">data</span><span class="o">.</span><span class="n">data</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="p">(</span><span class="s2">"done in </span><span class="si">%0.3f</span><span class="s2">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="p">()</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Best score: </span><span class="si">%0.3f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">grid_search</span><span class="o">.</span><span class="n">best_score_</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Best parameters set:"</span><span class="p">)</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="p">(</span><span class="s2">"</span><span class="se">\t</span><span class="si">%s</span><span class="s2">: </span><span class="si">%r</span><span class="s2">"</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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