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<div class="section" id="comparing-various-online-solvers">
<span id="example-linear-model-plot-sgd-comparison-py"></span><h1>Comparing various online solvers<a class="headerlink" href="#comparing-various-online-solvers" title="Permalink to this headline">¶</a></h1>
<p>An example showing how different online solvers perform
on the hand-written digits dataset.</p>
<img alt="../../_images/plot_sgd_comparison_001.png" class="align-center" src="../../_images/plot_sgd_comparison_001.png" />
<p><strong>Script output</strong>:</p>
<div class="highlight-python"><div class="highlight"><pre>training SGD
training ASGD
training Perceptron
training Passive-Aggressive I
training Passive-Aggressive II
training SAG
</pre></div>
</div>
<p><strong>Python source code:</strong> <a class="reference download internal" href="../../_downloads/plot_sgd_comparison.py"><code class="xref download docutils literal"><span class="pre">plot_sgd_comparison.py</span></code></a></p>
<div class="highlight-python"><div class="highlight"><pre><span class="c1"># Author: Rob Zinkov <rob at zinkov dot com></span>
<span class="c1"># License: BSD 3 clause</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span>
<span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.cross_validation.train_test_split.html#sklearn.cross_validation.train_test_split"><span class="n">train_test_split</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.SGDClassifier.html#sklearn.linear_model.SGDClassifier"><span class="n">SGDClassifier</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.linear_model.Perceptron.html#sklearn.linear_model.Perceptron"><span class="n">Perceptron</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.PassiveAggressiveClassifier.html#sklearn.linear_model.PassiveAggressiveClassifier"><span class="n">PassiveAggressiveClassifier</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.LogisticRegression.html#sklearn.linear_model.LogisticRegression"><span class="n">LogisticRegression</span></a>
<span class="n">heldout</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.95</span><span class="p">,</span> <span class="mf">0.90</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">,</span> <span class="mf">0.50</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">]</span>
<span class="n">rounds</span> <span class="o">=</span> <span class="mi">20</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</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">digits</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">digits</span><span class="o">.</span><span class="n">target</span>
<span class="n">classifiers</span> <span class="o">=</span> <span class="p">[</span>
<span class="p">(</span><span class="s2">"SGD"</span><span class="p">,</span> <a href="../../modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier"><span class="n">SGDClassifier</span></a><span class="p">()),</span>
<span class="p">(</span><span class="s2">"ASGD"</span><span class="p">,</span> <a href="../../modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier"><span class="n">SGDClassifier</span></a><span class="p">(</span><span class="n">average</span><span class="o">=</span><span class="bp">True</span><span class="p">)),</span>
<span class="p">(</span><span class="s2">"Perceptron"</span><span class="p">,</span> <a href="../../modules/generated/sklearn.linear_model.Perceptron.html#sklearn.linear_model.Perceptron"><span class="n">Perceptron</span></a><span class="p">()),</span>
<span class="p">(</span><span class="s2">"Passive-Aggressive I"</span><span class="p">,</span> <a href="../../modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html#sklearn.linear_model.PassiveAggressiveClassifier"><span class="n">PassiveAggressiveClassifier</span></a><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">'hinge'</span><span class="p">,</span>
<span class="n">C</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)),</span>
<span class="p">(</span><span class="s2">"Passive-Aggressive II"</span><span class="p">,</span> <a href="../../modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html#sklearn.linear_model.PassiveAggressiveClassifier"><span class="n">PassiveAggressiveClassifier</span></a><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">'squared_hinge'</span><span class="p">,</span>
<span class="n">C</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)),</span>
<span class="p">(</span><span class="s2">"SAG"</span><span class="p">,</span> <a href="../../modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression"><span class="n">LogisticRegression</span></a><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s1">'sag'</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mf">1e-1</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mf">1.e4</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">0</span><span class="p">]))</span>
<span class="p">]</span>
<span class="n">xx</span> <span class="o">=</span> <span class="mf">1.</span> <span class="o">-</span> <a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.array.html#numpy.array"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">(</span><span class="n">heldout</span><span class="p">)</span>
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">clf</span> <span class="ow">in</span> <span class="n">classifiers</span><span class="p">:</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"training </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="n">name</span><span class="p">)</span>
<span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
<span class="n">yy</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">heldout</span><span class="p">:</span>
<span class="n">yy_</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">rounds</span><span class="p">):</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> \
<a href="../../modules/generated/sklearn.cross_validation.train_test_split.html#sklearn.cross_validation.train_test_split"><span class="n">train_test_split</span></a><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="n">i</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">rng</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>
<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</span><span class="p">)</span>
<span class="n">yy_</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.mean.html#numpy.mean"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><span class="n">y_pred</span> <span class="o">==</span> <span class="n">y_test</span><span class="p">))</span>
<span class="n">yy</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.mean.html#numpy.mean"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><span class="n">yy_</span><span class="p">))</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot"><span class="n">plt</span><span class="o">.</span><span class="n">plot</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">label</span><span class="o">=</span><span class="n">name</span><span class="p">)</span>
<a href="http://matplotlib.org/api/legend_api.html#matplotlib.legend"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"upper right"</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.xlabel"><span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span></a><span class="p">(</span><span class="s2">"Proportion train"</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.ylabel"><span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span></a><span class="p">(</span><span class="s2">"Test Error Rate"</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
<p><strong>Total running time of the example:</strong> 9.30 seconds
( 0 minutes 9.30 seconds)</p>
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