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<li><a class="reference internal" href="#">SVM-Anova: SVM with univariate feature selection</a></li>
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  <div class="section" id="svm-anova-svm-with-univariate-feature-selection">
<span id="example-svm-plot-svm-anova-py"></span><h1>SVM-Anova: SVM with univariate feature selection<a class="headerlink" href="#svm-anova-svm-with-univariate-feature-selection" title="Permalink to this headline">ΒΆ</a></h1>
<p>This example shows how to perform univariate feature before running a SVC
(support vector classifier) to improve the classification scores.</p>
<img alt="auto_examples/svm/images/plot_svm_anova.png" class="align-center" src="auto_examples/svm/images/plot_svm_anova.png" />
<p><strong>Python source code:</strong> <a class="reference download internal" href="../../_downloads/plot_svm_anova.py"><tt class="xref download docutils literal"><span class="pre">plot_svm_anova.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="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pylab</span> <span class="kn">as</span> <span class="nn">pl</span>
<span class="kn">from</span> <span class="nn">scikits.learn</span> <span class="kn">import</span> <span class="n">svm</span><span class="p">,</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">feature_selection</span><span class="p">,</span> <span class="n">cross_val</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"># Import some data to play with</span>
<span class="n">digits</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_digits</span><span class="p">()</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">digits</span><span class="o">.</span><span class="n">target</span>
<span class="c"># Throw away data, to be in the curse of dimension settings</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="p">[:</span><span class="mi">200</span><span class="p">]</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">digits</span><span class="o">.</span><span class="n">data</span><span class="p">[:</span><span class="mi">200</span><span class="p">]</span>
<span class="n">n_samples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">n_samples</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span>
<span class="c"># add 200 non-informative features</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">X</span><span class="p">,</span> <span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="n">n_samples</span><span class="p">,</span> <span class="mi">200</span><span class="p">))))</span>

<span class="c">################################################################################</span>
<span class="c"># Create a feature-selection transform and an instance of SVM that we</span>
<span class="c"># combine together to have an full-blown estimator</span>

<span class="n">transform</span> <span class="o">=</span> <span class="n">feature_selection</span><span class="o">.</span><span class="n">SelectPercentile</span><span class="p">(</span><span class="n">feature_selection</span><span class="o">.</span><span class="n">f_classif</span><span class="p">)</span>

<span class="n">clf</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">([(</span><span class="s">&#39;anova&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="p">),</span> <span class="p">(</span><span class="s">&#39;svc&#39;</span><span class="p">,</span> <span class="n">svm</span><span class="o">.</span><span class="n">SVC</span><span class="p">())])</span>

<span class="c">################################################################################</span>
<span class="c"># Plot the cross-validation score as a function of percentile of features</span>
<span class="n">score_means</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
<span class="n">score_stds</span>  <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
<span class="n">percentiles</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">60</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>

<span class="k">for</span> <span class="n">percentile</span> <span class="ow">in</span> <span class="n">percentiles</span><span class="p">:</span>
    <span class="n">clf</span><span class="o">.</span><span class="n">_set_params</span><span class="p">(</span><span class="n">anova__percentile</span><span class="o">=</span><span class="n">percentile</span><span class="p">)</span>
    <span class="c"># Compute cross-validation score using all CPUs</span>
    <span class="n">this_scores</span> <span class="o">=</span> <span class="n">cross_val</span><span class="o">.</span><span class="n">cross_val_score</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</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">score_means</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">this_scores</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
    <span class="n">score_stds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">this_scores</span><span class="o">.</span><span class="n">std</span><span class="p">())</span>

<span class="n">pl</span><span class="o">.</span><span class="n">errorbar</span><span class="p">(</span><span class="n">percentiles</span><span class="p">,</span> <span class="n">score_means</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">score_stds</span><span class="p">))</span>

<span class="n">pl</span><span class="o">.</span><span class="n">title</span><span class="p">(</span>
    <span class="s">&#39;Performance of the SVM-Anova varying the percentile of features selected&#39;</span><span class="p">)</span>
<span class="n">pl</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s">&#39;Percentile&#39;</span><span class="p">)</span>
<span class="n">pl</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">&#39;Prediction rate&#39;</span><span class="p">)</span>

<span class="n">pl</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s">&#39;tight&#39;</span><span class="p">)</span>
<span class="n">pl</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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