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<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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<section id="putting-it-all-together">
<h1>Putting it all together<a class="headerlink" href="#putting-it-all-together" title="Permalink to this heading">¶</a></h1>
<section id="pipelining">
<h2>Pipelining<a class="headerlink" href="#pipelining" title="Permalink to this heading">¶</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>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">PCA</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchCV</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
<span class="c1"># Define a pipeline to search for the best combination of PCA truncation</span>
<span class="c1"># and classifier regularization.</span>
<span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">()</span>
<span class="c1"># Define a Standard Scaler to normalize inputs</span>
<span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
<span class="c1"># set the tolerance to a large value to make the example faster</span>
<span class="n">logistic</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">max_iter</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mf">0.1</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="s2">"scaler"</span><span class="p">,</span> <span class="n">scaler</span><span class="p">),</span> <span class="p">(</span><span class="s2">"pca"</span><span class="p">,</span> <span class="n">pca</span><span class="p">),</span> <span class="p">(</span><span class="s2">"logistic"</span><span class="p">,</span> <span class="n">logistic</span><span class="p">)])</span>
<span class="n">X_digits</span><span class="p">,</span> <span class="n">y_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">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># Parameters of pipelines can be set using '__' separated parameter names:</span>
<span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"pca__n_components"</span><span class="p">:</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">45</span><span class="p">,</span> <span class="mi">60</span><span class="p">],</span>
<span class="s2">"logistic__C"</span><span class="p">:</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">4</span><span class="p">),</span>
<span class="p">}</span>
<span class="n">search</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="n">param_grid</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">search</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="nb">print</span><span class="p">(</span><span class="s2">"Best parameter (CV score=</span><span class="si">%0.3f</span><span class="s2">):"</span> <span class="o">%</span> <span class="n">search</span><span class="o">.</span><span class="n">best_score_</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">search</span><span class="o">.</span><span class="n">best_params_</span><span class="p">)</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">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax0</span><span class="p">,</span> <span class="n">ax1</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">ax0</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
<span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">pca</span><span class="o">.</span><span class="n">n_components_</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="p">,</span> <span class="s2">"+"</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">ax0</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"PCA explained variance ratio"</span><span class="p">)</span>
<span class="n">ax0</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span>
<span class="n">search</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="s2">"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="s2">":"</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s2">"n_components chosen"</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
<a class="reference external image-reference" href="../../auto_examples/compose/plot_digits_pipe.html"><img alt="../../_images/sphx_glr_plot_digits_pipe_001.png" class="align-center" src="../../_images/sphx_glr_plot_digits_pipe_001.png" style="width: 390.0px; height: 390.0px;" /></a>
</section>
<section id="face-recognition-with-eigenfaces">
<h2>Face recognition with eigenfaces<a class="headerlink" href="#face-recognition-with-eigenfaces" title="Permalink to this heading">¶</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><p><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)</p>
</div></blockquote>
<div class="highlight-default notranslate"><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">"""</span>
<span class="c1"># %%</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">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="kn">import</span> <span class="n">train_test_split</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">RandomizedSearchCV</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_lfw_people</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">classification_report</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">ConfusionMatrixDisplay</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">PCA</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span>
<span class="kn">from</span> <span class="nn">sklearn.utils.fixes</span> <span class="kn">import</span> <span class="n">loguniform</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 and keep 25% of the data for testing.</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="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">scaler</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="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="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="s2">"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="s2">"C"</span><span class="p">:</span> <span class="n">loguniform</span><span class="p">(</span><span class="mf">1e3</span><span class="p">,</span> <span class="mf">1e5</span><span class="p">),</span>
<span class="s2">"gamma"</span><span class="p">:</span> <span class="n">loguniform</span><span class="p">(</span><span class="mf">1e-4</span><span class="p">,</span> <span class="mf">1e-1</span><span class="p">),</span>
<span class="p">}</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">RandomizedSearchCV</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="s2">"rbf"</span><span class="p">,</span> <span class="n">class_weight</span><span class="o">=</span><span class="s2">"balanced"</span><span class="p">),</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="mi">10</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="n">ConfusionMatrixDisplay</span><span class="o">.</span><span class="n">from_estimator</span><span class="p">(</span>
<span class="n">clf</span><span class="p">,</span> <span class="n">X_test_pca</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">display_labels</span><span class="o">=</span><span class="n">target_names</span><span class="p">,</span> <span class="n">xticks_rotation</span><span class="o">=</span><span class="s2">"vertical"</span>
<span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</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="w"> </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="mf">0.01</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="mf">0.99</span><span class="p">,</span> <span class="n">top</span><span class="o">=</span><span class="mf">0.90</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=</span><span class="mf">0.35</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"># %%</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="s2">" "</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="s2">" "</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="s2">"predicted: </span><span class="si">%s</span><span class="se">\n</span><span class="s2">true: </span><span class="si">%s</span><span class="s2">"</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="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"># %%</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>
<span class="c1"># %%</span>
<span class="c1"># Face recognition problem would be much more effectively solved by training</span>
<span class="c1"># convolutional neural networks but this family of models is outside of the scope of</span>
<span class="c1"># the scikit-learn library. Interested readers should instead try to use pytorch or</span>
<span class="c1"># tensorflow to implement such models.</span>
</pre></div>
</div>
<figure class="align-default" id="id1">
<a class="reference internal image-reference" href="../../_images/plot_face_recognition_1.png"><img alt="../../_images/plot_face_recognition_1.png" src="../../_images/plot_face_recognition_1.png" style="width: 360.0px; height: 359.5px;" /></a>
<figcaption>
<p><span class="caption-text"><strong>Prediction</strong></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</figcaption>
</figure>
<figure class="align-default" id="id2">
<a class="reference internal image-reference" href="../../_images/plot_face_recognition_2.png"><img alt="../../_images/plot_face_recognition_2.png" src="../../_images/plot_face_recognition_2.png" style="width: 360.0px; height: 359.5px;" /></a>
<figcaption>
<p><span class="caption-text"><strong>Eigenfaces</strong></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</figcaption>
</figure>
<p>Expected results for the top 5 most represented people in the dataset:</p>
<div class="highlight-default notranslate"><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>
</section>
<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 heading">¶</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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