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<li><a class="reference internal" href="#">Faces recognition example using eigenfaces and SVMs</a></li>
</ul>
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<div class="section" id="faces-recognition-example-using-eigenfaces-and-svms">
<span id="sphx-glr-auto-examples-applications-plot-face-recognition-py"></span><h1>Faces recognition example using eigenfaces and SVMs<a class="headerlink" href="#faces-recognition-example-using-eigenfaces-and-svms" title="Permalink to this headline">¶</a></h1>
<p>The dataset used in this example is a preprocessed excerpt of the
“Labeled Faces in the Wild”, aka <a class="reference external" href="http://vis-www.cs.umass.edu/lfw/">LFW</a>:</p>
<blockquote>
<div><a class="reference external" href="http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz">http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz</a> (233MB)</div></blockquote>
<p>Expected results for the top 5 most represented people in the dataset:</p>
<table border="1" class="docutils">
<colgroup>
<col width="33%" />
<col width="22%" />
<col width="13%" />
<col width="19%" />
<col width="13%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head"> </th>
<th class="head"> </th>
<th class="head"> </th>
<th class="head"> </th>
<th class="head"> </th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td>Ariel Sharon</td>
<td>0.67</td>
<td>0.92</td>
<td>0.77</td>
<td>13</td>
</tr>
<tr class="row-odd"><td>Colin Powell</td>
<td>0.75</td>
<td>0.78</td>
<td>0.76</td>
<td>60</td>
</tr>
<tr class="row-even"><td>Donald Rumsfeld</td>
<td>0.78</td>
<td>0.67</td>
<td>0.72</td>
<td>27</td>
</tr>
<tr class="row-odd"><td>George W Bush</td>
<td>0.86</td>
<td>0.86</td>
<td>0.86</td>
<td>146</td>
</tr>
<tr class="row-even"><td>Gerhard Schroeder</td>
<td>0.76</td>
<td>0.76</td>
<td>0.76</td>
<td>25</td>
</tr>
<tr class="row-odd"><td>Hugo Chavez</td>
<td>0.67</td>
<td>0.67</td>
<td>0.67</td>
<td>15</td>
</tr>
<tr class="row-even"><td>Tony Blair</td>
<td>0.81</td>
<td>0.69</td>
<td>0.75</td>
<td>36</td>
</tr>
<tr class="row-odd"><td>avg / total</td>
<td>0.80</td>
<td>0.80</td>
<td>0.80</td>
<td>322</td>
</tr>
</tbody>
</table>
<ul class="sphx-glr-horizontal">
<li><a class="first reference internal image-reference" href="../../_images/sphx_glr_plot_face_recognition_001.png"><img alt="../../_images/sphx_glr_plot_face_recognition_001.png" src="../../_images/sphx_glr_plot_face_recognition_001.png" style="width: 338.4px; height: 337.93px;" /></a>
</li>
<li><a class="first reference internal image-reference" href="../../_images/sphx_glr_plot_face_recognition_002.png"><img alt="../../_images/sphx_glr_plot_face_recognition_002.png" src="../../_images/sphx_glr_plot_face_recognition_002.png" style="width: 338.4px; height: 337.93px;" /></a>
</li>
</ul>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-default"><div class="highlight"><pre><span></span><span class="n">Total</span> <span class="n">dataset</span> <span class="n">size</span><span class="p">:</span>
<span class="n">n_samples</span><span class="p">:</span> <span class="mi">1288</span>
<span class="n">n_features</span><span class="p">:</span> <span class="mi">1850</span>
<span class="n">n_classes</span><span class="p">:</span> <span class="mi">7</span>
<span class="n">Extracting</span> <span class="n">the</span> <span class="n">top</span> <span class="mi">150</span> <span class="n">eigenfaces</span> <span class="kn">from</span> <span class="mi">966</span> <span class="n">faces</span>
<span class="n">done</span> <span class="ow">in</span> <span class="mf">0.233</span><span class="n">s</span>
<span class="n">Projecting</span> <span class="n">the</span> <span class="nb">input</span> <span class="n">data</span> <span class="n">on</span> <span class="n">the</span> <span class="n">eigenfaces</span> <span class="n">orthonormal</span> <span class="n">basis</span>
<span class="n">done</span> <span class="ow">in</span> <span class="mf">0.024</span><span class="n">s</span>
<span class="n">Fitting</span> <span class="n">the</span> <span class="n">classifier</span> <span class="n">to</span> <span class="n">the</span> <span class="n">training</span> <span class="nb">set</span>
<span class="n">done</span> <span class="ow">in</span> <span class="mf">23.793</span><span class="n">s</span>
<span class="n">Best</span> <span class="n">estimator</span> <span class="n">found</span> <span class="n">by</span> <span class="n">grid</span> <span class="n">search</span><span class="p">:</span>
<a href="../../modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="View documentation for sklearn.svm.SVC"><span class="n">SVC</span></a><span class="p">(</span><span class="n">C</span><span class="o">=</span><span class="mf">1000.0</span><span class="p">,</span> <span class="n">cache_size</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">class_weight</span><span class="o">=</span><span class="s1">'balanced'</span><span class="p">,</span> <span class="n">coef0</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
<span class="n">decision_function_shape</span><span class="o">=</span><span class="s1">'ovr'</span><span class="p">,</span> <span class="n">degree</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="s1">'rbf'</span><span class="p">,</span>
<span class="n">max_iter</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">probability</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">shrinking</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">tol</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">Predicting</span> <span class="n">people</span><span class="s1">'s names on the test set</span>
<span class="n">done</span> <span class="ow">in</span> <span class="mf">0.061</span><span class="n">s</span>
<span class="n">precision</span> <span class="n">recall</span> <span class="n">f1</span><span class="o">-</span><span class="n">score</span> <span class="n">support</span>
<span class="n">Ariel</span> <span class="n">Sharon</span> <span class="mf">0.53</span> <span class="mf">0.69</span> <span class="mf">0.60</span> <span class="mi">13</span>
<span class="n">Colin</span> <span class="n">Powell</span> <span class="mf">0.77</span> <span class="mf">0.85</span> <span class="mf">0.81</span> <span class="mi">60</span>
<span class="n">Donald</span> <span class="n">Rumsfeld</span> <span class="mf">0.80</span> <span class="mf">0.74</span> <span class="mf">0.77</span> <span class="mi">27</span>
<span class="n">George</span> <span class="n">W</span> <span class="n">Bush</span> <span class="mf">0.90</span> <span class="mf">0.89</span> <span class="mf">0.89</span> <span class="mi">146</span>
<span class="n">Gerhard</span> <span class="n">Schroeder</span> <span class="mf">0.84</span> <span class="mf">0.84</span> <span class="mf">0.84</span> <span class="mi">25</span>
<span class="n">Hugo</span> <span class="n">Chavez</span> <span class="mf">0.83</span> <span class="mf">0.67</span> <span class="mf">0.74</span> <span class="mi">15</span>
<span class="n">Tony</span> <span class="n">Blair</span> <span class="mf">0.84</span> <span class="mf">0.75</span> <span class="mf">0.79</span> <span class="mi">36</span>
<span class="n">avg</span> <span class="o">/</span> <span class="n">total</span> <span class="mf">0.84</span> <span class="mf">0.83</span> <span class="mf">0.83</span> <span class="mi">322</span>
<span class="p">[[</span> <span class="mi">9</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span><span class="p">]</span>
<span class="p">[</span> <span class="mi">1</span> <span class="mi">51</span> <span class="mi">1</span> <span class="mi">6</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">0</span><span class="p">]</span>
<span class="p">[</span> <span class="mi">5</span> <span class="mi">0</span> <span class="mi">20</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">0</span><span class="p">]</span>
<span class="p">[</span> <span class="mi">1</span> <span class="mi">10</span> <span class="mi">1</span> <span class="mi">130</span> <span class="mi">2</span> <span class="mi">0</span> <span class="mi">2</span><span class="p">]</span>
<span class="p">[</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">2</span> <span class="mi">21</span> <span class="mi">0</span> <span class="mi">1</span><span class="p">]</span>
<span class="p">[</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">1</span> <span class="mi">10</span> <span class="mi">2</span><span class="p">]</span>
<span class="p">[</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">1</span> <span class="mi">4</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">27</span><span class="p">]]</span>
</pre></div>
</div>
<div class="line-block">
<div class="line"><br /></div>
</div>
<div class="highlight-python"><div class="highlight"><pre><span></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">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">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.model_selection</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="View documentation for sklearn.model_selection.train_test_split"><span class="n">train_test_split</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.datasets</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.datasets.fetch_lfw_people.html#sklearn.datasets.fetch_lfw_people" title="View documentation for sklearn.datasets.fetch_lfw_people"><span class="n">fetch_lfw_people</span></a>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="View documentation for sklearn.metrics.classification_report"><span class="n">classification_report</span></a>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="View documentation for sklearn.metrics.confusion_matrix"><span class="n">confusion_matrix</span></a>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="View documentation for sklearn.decomposition.PCA"><span class="n">PCA</span></a>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="View documentation for sklearn.svm.SVC"><span class="n">SVC</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">%(message)s</span><span class="s1">'</span><span class="p">)</span>
<span class="c1"># #############################################################################</span>
<span class="c1"># Download the data, if not already on disk and load it as numpy arrays</span>
<span class="n">lfw_people</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.fetch_lfw_people.html#sklearn.datasets.fetch_lfw_people" title="View documentation for sklearn.datasets.fetch_lfw_people"><span class="n">fetch_lfw_people</span></a><span class="p">(</span><span class="n">min_faces_per_person</span><span class="o">=</span><span class="mi">70</span><span class="p">,</span> <span class="n">resize</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
<span class="c1"># introspect the images arrays to find the shapes (for plotting)</span>
<span class="n">n_samples</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">lfw_people</span><span class="o">.</span><span class="n">images</span><span class="o">.</span><span class="n">shape</span>
<span class="c1"># for machine learning we use the 2 data directly (as relative pixel</span>
<span class="c1"># positions info is ignored by this model)</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">lfw_people</span><span class="o">.</span><span class="n">data</span>
<span class="n">n_features</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="c1"># the label to predict is the id of the person</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">lfw_people</span><span class="o">.</span><span class="n">target</span>
<span class="n">target_names</span> <span class="o">=</span> <span class="n">lfw_people</span><span class="o">.</span><span class="n">target_names</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="n">target_names</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="p">(</span><span class="s2">"Total dataset size:"</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"n_samples: </span><span class="si">%d</span><span class="s2">"</span> <span class="o">%</span> <span class="n">n_samples</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"n_features: </span><span class="si">%d</span><span class="s2">"</span> <span class="o">%</span> <span class="n">n_features</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"n_classes: </span><span class="si">%d</span><span class="s2">"</span> <span class="o">%</span> <span class="n">n_classes</span><span class="p">)</span>
<span class="c1"># #############################################################################</span>
<span class="c1"># Split into a training set and a test set using a stratified k fold</span>
<span class="c1"># split into a training and testing set</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</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> <a href="../../modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="View documentation for sklearn.model_selection.train_test_split"><span class="n">train_test_split</span></a><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">test_size</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="c1"># #############################################################################</span>
<span class="c1"># Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled</span>
<span class="c1"># dataset): unsupervised feature extraction / dimensionality reduction</span>
<span class="n">n_components</span> <span class="o">=</span> <span class="mi">150</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Extracting the top </span><span class="si">%d</span><span class="s2"> eigenfaces from </span><span class="si">%d</span><span class="s2"> faces"</span>
<span class="o">%</span> <span class="p">(</span><span class="n">n_components</span><span class="p">,</span> <span class="n">X_train</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">t0</span> <span class="o">=</span> <span class="n">time</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" title="View documentation for sklearn.decomposition.PCA"><span class="n">PCA</span></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="n">n_components</span><span class="p">,</span> <span class="n">svd_solver</span><span class="o">=</span><span class="s1">'randomized'</span><span class="p">,</span>
<span class="n">whiten</span><span class="o">=</span><span class="bp">True</span><span class="p">)</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="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="n">eigenfaces</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">components_</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">n_components</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Projecting the input data on the eigenfaces orthonormal basis"</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">X_train_pca</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">X_test_pca</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</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="c1"># #############################################################################</span>
<span class="c1"># Train a SVM classification model</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Fitting the classifier to the training set"</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">param_grid</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'C'</span><span class="p">:</span> <span class="p">[</span><span class="mf">1e3</span><span class="p">,</span> <span class="mf">5e3</span><span class="p">,</span> <span class="mf">1e4</span><span class="p">,</span> <span class="mf">5e4</span><span class="p">,</span> <span class="mf">1e5</span><span class="p">],</span>
<span class="s1">'gamma'</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.0001</span><span class="p">,</span> <span class="mf">0.0005</span><span class="p">,</span> <span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.005</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">],</span> <span class="p">}</span>
<span class="n">clf</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><a href="../../modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="View documentation for sklearn.svm.SVC"><span class="n">SVC</span></a><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">'rbf'</span><span class="p">,</span> <span class="n">class_weight</span><span class="o">=</span><span class="s1">'balanced'</span><span class="p">),</span> <span class="n">param_grid</span><span class="p">)</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_pca</span><span class="p">,</span> <span class="n">y_train</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="s2">"Best estimator found by grid search:"</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">best_estimator_</span><span class="p">)</span>
<span class="c1"># #############################################################################</span>
<span class="c1"># Quantitative evaluation of the model quality on the test set</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Predicting people's names on the test set"</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">y_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_pca</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><a href="../../modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="View documentation for sklearn.metrics.classification_report"><span class="n">classification_report</span></a><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">target_names</span><span class="o">=</span><span class="n">target_names</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><a href="../../modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="View documentation for sklearn.metrics.confusion_matrix"><span class="n">confusion_matrix</span></a><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="n">n_classes</span><span class="p">)))</span>
<span class="c1"># #############################################################################</span>
<span class="c1"># Qualitative evaluation of the predictions using matplotlib</span>
<span class="k">def</span> <span class="nf">plot_gallery</span><span class="p">(</span><span class="n">images</span><span class="p">,</span> <span class="n">titles</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">n_row</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">n_col</span><span class="o">=</span><span class="mi">4</span><span class="p">):</span>
<span class="sd">"""Helper function to plot a gallery of portraits"""</span>
<a href="http://matplotlib.org/api/_as_gen/matplotlib.figure.AxesStack.html#matplotlib.figure" title="View documentation for matplotlib.pyplot.figure"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">1.8</span> <span class="o">*</span> <span class="n">n_col</span><span class="p">,</span> <span class="mf">2.4</span> <span class="o">*</span> <span class="n">n_row</span><span class="p">))</span>
<a href="http://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots_adjust.html#matplotlib.pyplot.subplots_adjust" title="View documentation for matplotlib.pyplot.subplots_adjust"><span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span></a><span class="p">(</span><span class="n">bottom</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">left</span><span class="o">=.</span><span class="mo">01</span><span class="p">,</span> <span class="n">right</span><span class="o">=.</span><span class="mi">99</span><span class="p">,</span> <span class="n">top</span><span class="o">=.</span><span class="mi">90</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=.</span><span class="mi">35</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_row</span> <span class="o">*</span> <span class="n">n_col</span><span class="p">):</span>
<a href="http://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot" title="View documentation for matplotlib.pyplot.subplot"><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span></a><span class="p">(</span><span class="n">n_row</span><span class="p">,</span> <span class="n">n_col</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<a href="http://matplotlib.org/api/_as_gen/matplotlib.pyplot.imshow.html#matplotlib.pyplot.imshow" title="View documentation for matplotlib.pyplot.imshow"><span class="n">plt</span><span class="o">.</span><span class="n">imshow</span></a><span class="p">(</span><span class="n">images</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">)),</span> <span class="n">cmap</span><span class="o">=</span><a href="http://matplotlib.org/api/_as_gen/matplotlib.pyplot.gray.html#matplotlib.pyplot.gray" title="View documentation for matplotlib.pyplot.cm.gray"><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">gray</span></a><span class="p">)</span>
<a href="http://matplotlib.org/api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="View documentation for matplotlib.pyplot.title"><span class="n">plt</span><span class="o">.</span><span class="n">title</span></a><span class="p">(</span><span class="n">titles</span><span class="p">[</span><span class="n">i</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/_as_gen/matplotlib.pyplot.xticks.html#matplotlib.pyplot.xticks" title="View documentation for matplotlib.pyplot.xticks"><span class="n">plt</span><span class="o">.</span><span class="n">xticks</span></a><span class="p">(())</span>
<a href="http://matplotlib.org/api/_as_gen/matplotlib.pyplot.yticks.html#matplotlib.pyplot.yticks" title="View documentation for matplotlib.pyplot.yticks"><span class="n">plt</span><span class="o">.</span><span class="n">yticks</span></a><span class="p">(())</span>
<span class="c1"># plot the result of the prediction on a portion of the test set</span>
<span class="k">def</span> <span class="nf">title</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">target_names</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span>
<span class="n">pred_name</span> <span class="o">=</span> <span class="n">target_names</span><span class="p">[</span><span class="n">y_pred</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span><span class="o">.</span><span class="n">rsplit</span><span class="p">(</span><span class="s1">' '</span><span class="p">,</span> <span class="mi">1</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">true_name</span> <span class="o">=</span> <span class="n">target_names</span><span class="p">[</span><span class="n">y_test</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span><span class="o">.</span><span class="n">rsplit</span><span class="p">(</span><span class="s1">' '</span><span class="p">,</span> <span class="mi">1</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="k">return</span> <span class="s1">'predicted: </span><span class="si">%s</span><span class="se">\n</span><span class="s1">true: </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="n">pred_name</span><span class="p">,</span> <span class="n">true_name</span><span class="p">)</span>
<span class="n">prediction_titles</span> <span class="o">=</span> <span class="p">[</span><span class="n">title</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">target_names</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">y_pred</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">plot_gallery</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">prediction_titles</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">)</span>
<span class="c1"># plot the gallery of the most significative eigenfaces</span>
<span class="n">eigenface_titles</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"eigenface </span><span class="si">%d</span><span class="s2">"</span> <span class="o">%</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">eigenfaces</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">plot_gallery</span><span class="p">(</span><span class="n">eigenfaces</span><span class="p">,</span> <span class="n">eigenface_titles</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">)</span>
<a href="http://matplotlib.org/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="View documentation for matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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