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<ul>
<li><a class="reference internal" href="#">Putting it all together</a><ul>
<li><a class="reference internal" href="#pipelining">Pipelining</a></li>
<li><a class="reference internal" href="#face-recognition-with-eigenfaces">Face recognition with eigenfaces</a></li>
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<div class="section" id="putting-it-all-together">
<h1>Putting it all together<a class="headerlink" href="#putting-it-all-together" title="Permalink to this headline">¶</a></h1>
<div class="section" id="pipelining">
<h2>Pipelining<a class="headerlink" href="#pipelining" title="Permalink to this headline">¶</a></h2>
<p>We have seen that some estimators can transform data and that some estimators
can predict variables. We can also create combined estimators:</p>
<a class="reference external image-reference" href="../../auto_examples/plot_digits_pipe.html"><img alt="../../_images/sphx_glr_plot_digits_pipe_001.png" class="align-right" src="../../_images/sphx_glr_plot_digits_pipe_001.png" style="width: 260.0px; height: 195.0px;" /></a>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">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="k">import</span> <span class="n">Pipeline</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">GridSearchCV</span>
<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> <span class="n">decomposition</span><span class="o">.</span><span class="n">PCA</span><span class="p">()</span>
<span class="n">pipe</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span><span class="n">steps</span><span class="o">=</span><span class="p">[(</span><span class="s1">'pca'</span><span class="p">,</span> <span class="n">pca</span><span class="p">),</span> <span class="p">(</span><span class="s1">'logistic'</span><span class="p">,</span> <span class="n">logistic</span><span class="p">)])</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">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="c1">###############################################################################</span>
<span class="c1"># 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>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><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>
<span class="n">plt</span><span class="o">.</span><span class="n">clf</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axes</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">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>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><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>
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">'tight'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'n_components'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'explained_variance_'</span><span class="p">)</span>
<span class="c1">###############################################################################</span>
<span class="c1"># 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> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><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="c1">#Parameters of pipelines can be set using ‘__’ separated parameter names:</span>
<span class="n">estimator</span> <span class="o">=</span> <span class="n">GridSearchCV</span><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>
<span class="n">plt</span><span class="o">.</span><span class="n">axvline</span><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="s1">'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="s1">':'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'n_components chosen'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><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>
</pre></div>
</div>
</div>
<div class="section" id="face-recognition-with-eigenfaces">
<h2>Face recognition with eigenfaces<a class="headerlink" href="#face-recognition-with-eigenfaces" title="Permalink to this headline">¶</a></h2>
<p>The dataset used in this example is a preprocessed excerpt of the
“Labeled Faces in the Wild”, also known as <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>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="sd">"""</span>
<span class="sd">===================================================</span>
<span class="sd">Faces recognition example using eigenfaces and SVMs</span>
<span class="sd">===================================================</span>
<span class="sd">The dataset used in this example is a preprocessed excerpt of the</span>
<span class="sd">"Labeled Faces in the Wild", aka LFW_:</span>
<span class="sd"> http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233MB)</span>
<span class="sd">.. _LFW: http://vis-www.cs.umass.edu/lfw/</span>
<span class="sd">Expected results for the top 5 most represented people in the dataset:</span>
<span class="sd">================== ============ ======= ========== =======</span>
<span class="sd"> precision recall f1-score support</span>
<span class="sd">================== ============ ======= ========== =======</span>
<span class="sd"> Ariel Sharon 0.67 0.92 0.77 13</span>
<span class="sd"> Colin Powell 0.75 0.78 0.76 60</span>
<span class="sd"> Donald Rumsfeld 0.78 0.67 0.72 27</span>
<span class="sd"> George W Bush 0.86 0.86 0.86 146</span>
<span class="sd">Gerhard Schroeder 0.76 0.76 0.76 25</span>
<span class="sd"> Hugo Chavez 0.67 0.67 0.67 15</span>
<span class="sd"> Tony Blair 0.81 0.69 0.75 36</span>
<span class="sd"> avg / total 0.80 0.80 0.80 322</span>
<span class="sd">================== ============ ======= ========== =======</span>
<span class="sd">"""</span>
<span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">print_function</span>
<span class="kn">from</span> <span class="nn">time</span> <span class="k">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="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">train_test_split</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">GridSearchCV</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="k">import</span> <span class="n">fetch_lfw_people</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">classification_report</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">confusion_matrix</span>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="k">import</span> <span class="n">PCA</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="k">import</span> <span class="n">SVC</span>
<span class="nb">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="nb">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> <span class="n">fetch_lfw_people</span><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="nb">print</span><span class="p">(</span><span class="s2">"Total dataset size:"</span><span class="p">)</span>
<span class="nb">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="nb">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="nb">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> <span class="n">train_test_split</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">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="nb">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> <span class="n">PCA</span><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="kc">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="nb">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="nb">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="nb">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="nb">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> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">SVC</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">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="nb">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="nb">print</span><span class="p">(</span><span class="s2">"Best estimator found by grid search:"</span><span class="p">)</span>
<span class="nb">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="nb">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="nb">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="nb">print</span><span class="p">(</span><span class="n">classification_report</span><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="nb">print</span><span class="p">(</span><span class="n">confusion_matrix</span><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>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><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>
<span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><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="mi">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>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><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>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><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><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">gray</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><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>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><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>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
<table border="1" class="centered docutils">
<colgroup>
<col width="50%" />
<col width="50%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="../../_images/plot_face_recognition_1.png"><img alt="prediction" src="../../_images/plot_face_recognition_1.png" style="width: 360.0px; height: 359.5px;" /></a></td>
<td><a class="reference internal" href="../../_images/plot_face_recognition_2.png"><img alt="eigenfaces" src="../../_images/plot_face_recognition_2.png" style="width: 360.0px; height: 359.5px;" /></a></td>
</tr>
<tr class="row-even"><td><strong>Prediction</strong></td>
<td><strong>Eigenfaces</strong></td>
</tr>
</tbody>
</table>
<p>Expected results for the top 5 most represented people in the dataset:</p>
<div class="highlight-default"><div class="highlight"><pre><span></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">Gerhard_Schroeder</span> <span class="mf">0.91</span> <span class="mf">0.75</span> <span class="mf">0.82</span> <span class="mi">28</span>
<span class="n">Donald_Rumsfeld</span> <span class="mf">0.84</span> <span class="mf">0.82</span> <span class="mf">0.83</span> <span class="mi">33</span>
<span class="n">Tony_Blair</span> <span class="mf">0.65</span> <span class="mf">0.82</span> <span class="mf">0.73</span> <span class="mi">34</span>
<span class="n">Colin_Powell</span> <span class="mf">0.78</span> <span class="mf">0.88</span> <span class="mf">0.83</span> <span class="mi">58</span>
<span class="n">George_W_Bush</span> <span class="mf">0.93</span> <span class="mf">0.86</span> <span class="mf">0.90</span> <span class="mi">129</span>
<span class="n">avg</span> <span class="o">/</span> <span class="n">total</span> <span class="mf">0.86</span> <span class="mf">0.84</span> <span class="mf">0.85</span> <span class="mi">282</span>
</pre></div>
</div>
</div>
<div class="section" id="open-problem-stock-market-structure">
<h2>Open problem: Stock Market Structure<a class="headerlink" href="#open-problem-stock-market-structure" title="Permalink to this headline">¶</a></h2>
<p>Can we predict the variation in stock prices for Google over a given time frame?</p>
<p><a class="reference internal" href="../../auto_examples/applications/plot_stock_market.html#stock-market"><span class="std std-ref">Learning a graph structure</span></a></p>
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