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<section class="sphx-glr-example-title" id="svm-exercise">
<span id="sphx-glr-auto-examples-exercises-plot-iris-exercise-py"></span><h1>SVM Exercise<a class="headerlink" href="#svm-exercise" title="Permalink to this heading">¶</a></h1>
<p>A tutorial exercise for using different SVM kernels.</p>
<p>This exercise is used in the <a class="reference internal" href="../../tutorial/statistical_inference/supervised_learning.html#using-kernels-tut"><span class="std std-ref">Using kernels</span></a> part of the
<a class="reference internal" href="../../tutorial/statistical_inference/supervised_learning.html#supervised-learning-tut"><span class="std std-ref">Supervised learning: predicting an output variable from high-dimensional observations</span></a> section of the <a class="reference internal" href="../../tutorial/statistical_inference/index.html#stat-learn-tut-index"><span class="std std-ref">A tutorial on statistical-learning for scientific data processing</span></a>.</p>
<ul class="sphx-glr-horizontal">
<li><img src="../../_images/sphx_glr_plot_iris_exercise_001.png" srcset="../../_images/sphx_glr_plot_iris_exercise_001.png" alt="linear" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_plot_iris_exercise_002.png" srcset="../../_images/sphx_glr_plot_iris_exercise_002.png" alt="rbf" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_plot_iris_exercise_003.png" srcset="../../_images/sphx_glr_plot_iris_exercise_003.png" alt="poly" class = "sphx-glr-multi-img"/></li>
</ul>
<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</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">svm</span>
<span class="n">iris</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris" title="sklearn.datasets.load_iris" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">datasets</span><span class="o">.</span><span class="n">load_iris</span></a><span class="p">()</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">data</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">target</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">y</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:</span><span class="mi">2</span><span class="p">]</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="n">y</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">n_sample</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<a href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.seed.html#numpy.random.seed" title="numpy.random.seed" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span></a><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">order</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.permutation.html#numpy.random.permutation" title="numpy.random.permutation" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">permutation</span></a><span class="p">(</span><span class="n">n_sample</span><span class="p">)</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">order</span><span class="p">]</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="n">order</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:</span> <span class="nb">int</span><span class="p">(</span><span class="mf">0.9</span> <span class="o">*</span> <span class="n">n_sample</span><span class="p">)]</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">y</span><span class="p">[:</span> <span class="nb">int</span><span class="p">(</span><span class="mf">0.9</span> <span class="o">*</span> <span class="n">n_sample</span><span class="p">)]</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="mf">0.9</span> <span class="o">*</span> <span class="n">n_sample</span><span class="p">)</span> <span class="p">:]</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="mf">0.9</span> <span class="o">*</span> <span class="n">n_sample</span><span class="p">)</span> <span class="p">:]</span>
<span class="c1"># fit the model</span>
<span class="k">for</span> <span class="n">kernel</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">"linear"</span><span class="p">,</span> <span class="s2">"rbf"</span><span class="p">,</span> <span class="s2">"poly"</span><span class="p">):</span>
<span class="n">clf</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">svm</span><span class="o">.</span><span class="n">SVC</span></a><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">gamma</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">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>
<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>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.clf.html#matplotlib.pyplot.clf" title="matplotlib.pyplot.clf" 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">clf</span></a><span class="p">()</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html#matplotlib.pyplot.scatter" title="matplotlib.pyplot.scatter" 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">scatter</span></a><span class="p">(</span>
<span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">10</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">Paired</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">"k"</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">20</span>
<span class="p">)</span>
<span class="c1"># Circle out the test data</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html#matplotlib.pyplot.scatter" title="matplotlib.pyplot.scatter" 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">scatter</span></a><span class="p">(</span>
<span class="n">X_test</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X_test</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">s</span><span class="o">=</span><span class="mi">80</span><span class="p">,</span> <span class="n">facecolors</span><span class="o">=</span><span class="s2">"none"</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">"k"</span>
<span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.axis.html#matplotlib.pyplot.axis" title="matplotlib.pyplot.axis" 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">axis</span></a><span class="p">(</span><span class="s2">"tight"</span><span class="p">)</span>
<span class="n">x_min</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
<span class="n">x_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
<span class="n">y_min</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
<span class="n">y_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
<span class="n">XX</span><span class="p">,</span> <span class="n">YY</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.mgrid.html#numpy.mgrid" title="numpy.mgrid" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">mgrid</span></a><span class="p">[</span><span class="n">x_min</span><span class="p">:</span><span class="n">x_max</span><span class="p">:</span><span class="mi">200</span><span class="n">j</span><span class="p">,</span> <span class="n">y_min</span><span class="p">:</span><span class="n">y_max</span><span class="p">:</span><span class="mi">200</span><span class="n">j</span><span class="p">]</span>
<span class="n">Z</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.c_.html#numpy.c_" title="numpy.c_" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">c_</span></a><span class="p">[</span><span class="n">XX</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">YY</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])</span>
<span class="c1"># Put the result into a color plot</span>
<span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">XX</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.pcolormesh.html#matplotlib.pyplot.pcolormesh" title="matplotlib.pyplot.pcolormesh" 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">pcolormesh</span></a><span class="p">(</span><span class="n">XX</span><span class="p">,</span> <span class="n">YY</span><span class="p">,</span> <span class="n">Z</span> <span class="o">></span> <span class="mi">0</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">Paired</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.contour.html#matplotlib.pyplot.contour" title="matplotlib.pyplot.contour" 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">contour</span></a><span class="p">(</span>
<span class="n">XX</span><span class="p">,</span>
<span class="n">YY</span><span class="p">,</span>
<span class="n">Z</span><span class="p">,</span>
<span class="n">colors</span><span class="o">=</span><span class="p">[</span><span class="s2">"k"</span><span class="p">,</span> <span class="s2">"k"</span><span class="p">,</span> <span class="s2">"k"</span><span class="p">],</span>
<span class="n">linestyles</span><span class="o">=</span><span class="p">[</span><span class="s2">"--"</span><span class="p">,</span> <span class="s2">"-"</span><span class="p">,</span> <span class="s2">"--"</span><span class="p">],</span>
<span class="n">levels</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">],</span>
<span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="matplotlib.pyplot.title" 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">title</span></a><span class="p">(</span><span class="n">kernel</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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