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</div> <div class="content"> <div class="documentwrapper"> <div class="bodywrapper"> <div class="body"> <div class="section" id="path-with-l1-logistic-regression"> <span id="example-linear-model-plot-logistic-path-py"></span><h1>Path with L1- Logistic Regression<a class="headerlink" href="#path-with-l1-logistic-regression" title="Permalink to this headline">ΒΆ</a></h1> <p>Computes path on IRIS dataset.</p> <img alt="auto_examples/linear_model/images/plot_logistic_path.png" class="align-center" src="auto_examples/linear_model/images/plot_logistic_path.png" /> <p><strong>Python source code:</strong> <a class="reference download internal" href="../../_downloads/plot_logistic_path.py"><tt class="xref download docutils literal"><span class="pre">plot_logistic_path.py</span></tt></a></p> <div class="highlight-python"><div class="highlight"><pre><span class="k">print</span> <span class="n">__doc__</span> <span class="c"># Author: Alexandre Gramfort <alexandre.gramfort@inria.fr></span> <span class="c"># License: BSD Style.</span> <span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</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">pylab</span> <span class="kn">as</span> <span class="nn">pl</span> <span class="kn">from</span> <span class="nn">scikits.learn</span> <span class="kn">import</span> <span class="n">linear_model</span> <span class="kn">from</span> <span class="nn">scikits.learn</span> <span class="kn">import</span> <span class="n">datasets</span> <span class="n">iris</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_iris</span><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">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">2</span><span class="p">]</span> <span class="n">X</span> <span class="o">-=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="c">################################################################################</span> <span class="c"># Demo path functions</span> <span class="n">alphas</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="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span> <span class="k">print</span> <span class="s">"Computing regularization path ..."</span> <span class="n">start</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span> <span class="n">clf</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">C</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">penalty</span><span class="o">=</span><span class="s">'l1'</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">)</span> <span class="n">coefs_</span> <span class="o">=</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</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mf">1.0</span><span class="o">/</span><span class="n">alpha</span><span class="p">)</span><span class="o">.</span><span class="n">coef_</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> <span class="k">for</span> <span class="n">alpha</span> <span class="ow">in</span> <span class="n">alphas</span><span class="p">]</span> <span class="k">print</span> <span class="s">"This took "</span><span class="p">,</span> <span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span> <span class="n">coefs_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">coefs_</span><span class="p">)</span> <span class="n">pl</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">alphas</span><span class="p">),</span> <span class="n">coefs_</span><span class="p">)</span> <span class="n">ymin</span><span class="p">,</span> <span class="n">ymax</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">ylim</span><span class="p">()</span> <span class="n">pl</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s">'-log(alpha)'</span><span class="p">)</span> <span class="n">pl</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">'Coefficients'</span><span class="p">)</span> <span class="n">pl</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s">'Logistic Regression Path'</span><span class="p">)</span> <span class="n">pl</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s">'tight'</span><span class="p">)</span> <span class="n">pl</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> </pre></div> </div> </div> </div> </div> </div> <div class="clearer"></div> </div> </div> <div class="footer"> <p style="text-align: center">This documentation is relative to scikits.learn version 0.6.0<p> © 2010, scikits.learn developers (BSD Lincense). 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