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<div class="section" id="pipelining-chaining-a-pca-and-a-logistic-regression">
<span id="example-plot-digits-pipe-py"></span><h1>Pipelining: chaining a PCA and a logistic regression<a class="headerlink" href="#pipelining-chaining-a-pca-and-a-logistic-regression" title="Permalink to this headline">¶</a></h1>
<p>The PCA does an unsupervised dimensionality reduction, while the logistic
regression does the prediction.</p>
<p>We use a GridSearchCV to set the dimensionality of the PCA</p>
<img alt="../_images/plot_digits_pipe_0011.png" class="align-center" src="../_images/plot_digits_pipe_0011.png" />
<p><strong>Python source code:</strong> <a class="reference download internal" href="../_downloads/plot_digits_pipe.py"><tt class="xref download docutils literal"><span class="pre">plot_digits_pipe.py</span></tt></a></p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="n">__doc__</span><span class="p">)</span>
<span class="c"># Code source: Gaël Varoquaux</span>
<span class="c"># Modified for documentation by Jaques Grobler</span>
<span class="c"># License: BSD 3 clause</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">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">linear_model</span><span class="p">,</span> <span class="n">decomposition</span><span class="p">,</span> <span class="n">datasets</span>
<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"><span class="n">Pipeline</span></a>
<span class="kn">from</span> <span class="nn">sklearn.grid_search</span> <span class="kn">import</span> <a href="../modules/generated/sklearn.grid_search.GridSearchCV.html#sklearn.grid_search.GridSearchCV"><span class="n">GridSearchCV</span></a>
<span class="n">logistic</span> <span class="o">=</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">LogisticRegression</span><span class="p">()</span>
<span class="n">pca</span> <span class="o">=</span> <a href="../modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA"><span class="n">decomposition</span><span class="o">.</span><span class="n">PCA</span></a><span class="p">()</span>
<span class="n">pipe</span> <span class="o">=</span> <a href="../modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline"><span class="n">Pipeline</span></a><span class="p">(</span><span class="n">steps</span><span class="o">=</span><span class="p">[(</span><span class="s">'pca'</span><span class="p">,</span> <span class="n">pca</span><span class="p">),</span> <span class="p">(</span><span class="s">'logistic'</span><span class="p">,</span> <span class="n">logistic</span><span class="p">)])</span>
<span class="n">digits</span> <span class="o">=</span> <a href="../modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits"><span class="n">datasets</span><span class="o">.</span><span class="n">load_digits</span></a><span class="p">()</span>
<span class="n">X_digits</span> <span class="o">=</span> <span class="n">digits</span><span class="o">.</span><span class="n">data</span>
<span class="n">y_digits</span> <span class="o">=</span> <span class="n">digits</span><span class="o">.</span><span class="n">target</span>
<span class="c">###############################################################################</span>
<span class="c"># Plot the PCA spectrum</span>
<span class="n">pca</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_digits</span><span class="p">)</span>
<a href="http://matplotlib.org/api/figure_api.html#matplotlib.figure"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.clf"><span class="n">plt</span><span class="o">.</span><span class="n">clf</span></a><span class="p">()</span>
<a href="http://matplotlib.org/api/axes_api.html#matplotlib.axes"><span class="n">plt</span><span class="o">.</span><span class="n">axes</span></a><span class="p">([</span><span class="o">.</span><span class="mi">2</span><span class="p">,</span> <span class="o">.</span><span class="mi">2</span><span class="p">,</span> <span class="o">.</span><span class="mi">7</span><span class="p">,</span> <span class="o">.</span><span class="mi">7</span><span class="p">])</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<a href="http://matplotlib.org/api/axis_api.html#matplotlib.axis"><span class="n">plt</span><span class="o">.</span><span class="n">axis</span></a><span class="p">(</span><span class="s">'tight'</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.xlabel"><span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span></a><span class="p">(</span><span class="s">'n_components'</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.ylabel"><span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span></a><span class="p">(</span><span class="s">'explained_variance_'</span><span class="p">)</span>
<span class="c">###############################################################################</span>
<span class="c"># Prediction</span>
<span class="n">n_components</span> <span class="o">=</span> <span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">64</span><span class="p">]</span>
<span class="n">Cs</span> <span class="o">=</span> <a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.logspace.html#numpy.logspace"><span class="n">np</span><span class="o">.</span><span class="n">logspace</span></a><span class="p">(</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="c">#Parameters of pipelines can be set using ‘__’ separated parameter names:</span>
<span class="n">estimator</span> <span class="o">=</span> <a href="../modules/generated/sklearn.grid_search.GridSearchCV.html#sklearn.grid_search.GridSearchCV"><span class="n">GridSearchCV</span></a><span class="p">(</span><span class="n">pipe</span><span class="p">,</span>
<span class="nb">dict</span><span class="p">(</span><span class="n">pca__n_components</span><span class="o">=</span><span class="n">n_components</span><span class="p">,</span>
<span class="n">logistic__C</span><span class="o">=</span><span class="n">Cs</span><span class="p">))</span>
<span class="n">estimator</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_digits</span><span class="p">,</span> <span class="n">y_digits</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.axvline"><span class="n">plt</span><span class="o">.</span><span class="n">axvline</span></a><span class="p">(</span><span class="n">estimator</span><span class="o">.</span><span class="n">best_estimator_</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s">'pca'</span><span class="p">]</span><span class="o">.</span><span class="n">n_components</span><span class="p">,</span>
<span class="n">linestyle</span><span class="o">=</span><span class="s">':'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">'n_components chosen'</span><span class="p">)</span>
<a href="http://matplotlib.org/api/legend_api.html#matplotlib.legend"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span><span class="n">prop</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">12</span><span class="p">))</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
<p><strong>Total running time of the example:</strong> 8.43 seconds
( 0 minutes 8.43 seconds)</p>
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