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<li><a class="reference internal" href="#">Face completion with a multi-output estimators</a></li>
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to download the full example code or to run this example in your browser via Binder</p>
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<section class="sphx-glr-example-title" id="face-completion-with-a-multi-output-estimators">
<span id="sphx-glr-auto-examples-miscellaneous-plot-multioutput-face-completion-py"></span><h1>Face completion with a multi-output estimators<a class="headerlink" href="#face-completion-with-a-multi-output-estimators" title="Permalink to this heading">¶</a></h1>
<p>This example shows the use of multi-output estimator to complete images.
The goal is to predict the lower half of a face given its upper half.</p>
<p>The first column of images shows true faces. The next columns illustrate
how extremely randomized trees, k nearest neighbors, linear
regression and ridge regression complete the lower half of those faces.</p>
<img src="../../_images/sphx_glr_plot_multioutput_face_completion_001.png" srcset="../../_images/sphx_glr_plot_multioutput_face_completion_001.png" alt="Face completion with multi-output estimators, true faces, Extra trees, K-nn, Linear regression, Ridge" class = "sphx-glr-single-img"/><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</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.datasets</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.datasets.fetch_olivetti_faces.html#sklearn.datasets.fetch_olivetti_faces" title="sklearn.datasets.fetch_olivetti_faces" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">fetch_olivetti_faces</span></a>
<span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.utils.check_random_state.html#sklearn.utils.check_random_state" title="sklearn.utils.check_random_state" class="sphx-glr-backref-module-sklearn-utils sphx-glr-backref-type-py-function"><span class="n">check_random_state</span></a>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.ensemble.ExtraTreesRegressor.html#sklearn.ensemble.ExtraTreesRegressor" title="sklearn.ensemble.ExtraTreesRegressor" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ExtraTreesRegressor</span></a>
<span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.neighbors.KNeighborsRegressor.html#sklearn.neighbors.KNeighborsRegressor" title="sklearn.neighbors.KNeighborsRegressor" class="sphx-glr-backref-module-sklearn-neighbors sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KNeighborsRegressor</span></a>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression" title="sklearn.linear_model.LinearRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LinearRegression</span></a>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.linear_model.RidgeCV.html#sklearn.linear_model.RidgeCV" title="sklearn.linear_model.RidgeCV" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RidgeCV</span></a>
<span class="c1"># Load the faces datasets</span>
<span class="n">data</span><span class="p">,</span> <span class="n">targets</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.fetch_olivetti_faces.html#sklearn.datasets.fetch_olivetti_faces" title="sklearn.datasets.fetch_olivetti_faces" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">fetch_olivetti_faces</span></a><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="n">train</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">targets</span> <span class="o"><</span> <span class="mi">30</span><span class="p">]</span>
<span class="n">test</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">targets</span> <span class="o">>=</span> <span class="mi">30</span><span class="p">]</span> <span class="c1"># Test on independent people</span>
<span class="c1"># Test on a subset of people</span>
<span class="n">n_faces</span> <span class="o">=</span> <span class="mi">5</span>
<span class="n">rng</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.utils.check_random_state.html#sklearn.utils.check_random_state" title="sklearn.utils.check_random_state" class="sphx-glr-backref-module-sklearn-utils sphx-glr-backref-type-py-function"><span class="n">check_random_state</span></a><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
<span class="n">face_ids</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">test</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">size</span><span class="o">=</span><span class="p">(</span><span class="n">n_faces</span><span class="p">,))</span>
<span class="n">test</span> <span class="o">=</span> <span class="n">test</span><span class="p">[</span><span class="n">face_ids</span><span class="p">,</span> <span class="p">:]</span>
<span class="n">n_pixels</span> <span class="o">=</span> <span class="n">data</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"># Upper half of the faces</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">train</span><span class="p">[:,</span> <span class="p">:</span> <span class="p">(</span><span class="n">n_pixels</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">]</span>
<span class="c1"># Lower half of the faces</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">train</span><span class="p">[:,</span> <span class="n">n_pixels</span> <span class="o">//</span> <span class="mi">2</span> <span class="p">:]</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">test</span><span class="p">[:,</span> <span class="p">:</span> <span class="p">(</span><span class="n">n_pixels</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">]</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">test</span><span class="p">[:,</span> <span class="n">n_pixels</span> <span class="o">//</span> <span class="mi">2</span> <span class="p">:]</span>
<span class="c1"># Fit estimators</span>
<span class="n">ESTIMATORS</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"Extra trees"</span><span class="p">:</span> <a href="../../modules/generated/sklearn.ensemble.ExtraTreesRegressor.html#sklearn.ensemble.ExtraTreesRegressor" title="sklearn.ensemble.ExtraTreesRegressor" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ExtraTreesRegressor</span></a><span class="p">(</span>
<span class="n">n_estimators</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">max_features</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span>
<span class="p">),</span>
<span class="s2">"K-nn"</span><span class="p">:</span> <a href="../../modules/generated/sklearn.neighbors.KNeighborsRegressor.html#sklearn.neighbors.KNeighborsRegressor" title="sklearn.neighbors.KNeighborsRegressor" class="sphx-glr-backref-module-sklearn-neighbors sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KNeighborsRegressor</span></a><span class="p">(),</span>
<span class="s2">"Linear regression"</span><span class="p">:</span> <a href="../../modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression" title="sklearn.linear_model.LinearRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LinearRegression</span></a><span class="p">(),</span>
<span class="s2">"Ridge"</span><span class="p">:</span> <a href="../../modules/generated/sklearn.linear_model.RidgeCV.html#sklearn.linear_model.RidgeCV" title="sklearn.linear_model.RidgeCV" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RidgeCV</span></a><span class="p">(),</span>
<span class="p">}</span>
<span class="n">y_test_predict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">estimator</span> <span class="ow">in</span> <span class="n">ESTIMATORS</span><span class="o">.</span><span class="n">items</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_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">y_test_predict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">estimator</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="c1"># Plot the completed faces</span>
<span class="n">image_shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">)</span>
<span class="n">n_cols</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">ESTIMATORS</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="matplotlib.pyplot.figure" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><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">2.0</span> <span class="o">*</span> <span class="n">n_cols</span><span class="p">,</span> <span class="mf">2.26</span> <span class="o">*</span> <span class="n">n_faces</span><span class="p">))</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.suptitle.html#matplotlib.pyplot.suptitle" title="matplotlib.pyplot.suptitle" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span></a><span class="p">(</span><span class="s2">"Face completion with multi-output estimators"</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">16</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_faces</span><span class="p">):</span>
<span class="n">true_face</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.hstack.html#numpy.hstack" title="numpy.hstack" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">hstack</span></a><span class="p">((</span><span class="n">X_test</span><span class="p">[</span><span class="n">i</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="k">if</span> <span class="n">i</span><span class="p">:</span>
<span class="n">sub</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot" title="matplotlib.pyplot.subplot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span></a><span class="p">(</span><span class="n">n_faces</span><span class="p">,</span> <span class="n">n_cols</span><span class="p">,</span> <span class="n">i</span> <span class="o">*</span> <span class="n">n_cols</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">sub</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot" title="matplotlib.pyplot.subplot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span></a><span class="p">(</span><span class="n">n_faces</span><span class="p">,</span> <span class="n">n_cols</span><span class="p">,</span> <span class="n">i</span> <span class="o">*</span> <span class="n">n_cols</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">"true faces"</span><span class="p">)</span>
<span class="n">sub</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">)</span>
<span class="n">sub</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span>
<span class="n">true_face</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">image_shape</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">interpolation</span><span class="o">=</span><span class="s2">"nearest"</span>
<span class="p">)</span>
<span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">est</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">ESTIMATORS</span><span class="p">)):</span>
<span class="n">completed_face</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.hstack.html#numpy.hstack" title="numpy.hstack" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">hstack</span></a><span class="p">((</span><span class="n">X_test</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">y_test_predict</span><span class="p">[</span><span class="n">est</span><span class="p">][</span><span class="n">i</span><span class="p">]))</span>
<span class="k">if</span> <span class="n">i</span><span class="p">:</span>
<span class="n">sub</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot" title="matplotlib.pyplot.subplot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span></a><span class="p">(</span><span class="n">n_faces</span><span class="p">,</span> <span class="n">n_cols</span><span class="p">,</span> <span class="n">i</span> <span class="o">*</span> <span class="n">n_cols</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">j</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">sub</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot" title="matplotlib.pyplot.subplot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span></a><span class="p">(</span><span class="n">n_faces</span><span class="p">,</span> <span class="n">n_cols</span><span class="p">,</span> <span class="n">i</span> <span class="o">*</span> <span class="n">n_cols</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">j</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">est</span><span class="p">)</span>
<span class="n">sub</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">)</span>
<span class="n">sub</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span>
<span class="n">completed_face</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">image_shape</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">interpolation</span><span class="o">=</span><span class="s2">"nearest"</span><span class="p">,</span>
<span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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