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</div> <div class="content"> <div class="documentwrapper"> <div class="bodywrapper"> <div class="body"> <div class="section" id="receiver-operating-characteristic-roc-with-cross-validation"> <span id="example-plot-roc-crossval-py"></span><h1>Receiver operating characteristic (ROC) with cross validation<a class="headerlink" href="#receiver-operating-characteristic-roc-with-cross-validation" title="Permalink to this headline">ΒΆ</a></h1> <p>Example of Receiver operating characteristic (ROC) metric to evaluate the quality of the output of a classifier using cross-validation.</p> <img alt="auto_examples/images/plot_roc_crossval.png" class="align-center" src="auto_examples/images/plot_roc_crossval.png" /> <p><strong>Python source code:</strong> <a class="reference download internal" href="../_downloads/plot_roc_crossval.py"><tt class="xref download docutils literal"><span class="pre">plot_roc_crossval.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="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span> <span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">interp</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">svm</span><span class="p">,</span> <span class="n">datasets</span> <span class="kn">from</span> <span class="nn">scikits.learn.metrics</span> <span class="kn">import</span> <span class="n">roc_curve</span><span class="p">,</span> <span class="n">auc</span> <span class="kn">from</span> <span class="nn">scikits.learn.cross_val</span> <span class="kn">import</span> <span class="n">StratifiedKFold</span> <span class="c">################################################################################</span> <span class="c"># Data IO and generation</span> <span class="c"># import some data to play with</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="p">,</span> <span class="n">y</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="p">[</span><span class="n">y</span><span class="o">!=</span><span class="mi">2</span><span class="p">]</span> <span class="n">n_samples</span><span class="p">,</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="c"># Add noisy features</span> <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">X</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">n_samples</span><span class="p">,</span> <span class="mi">200</span><span class="o">*</span><span class="n">n_features</span><span class="p">)]</span> <span class="c">################################################################################</span> <span class="c"># Classification and ROC analysis</span> <span class="c"># Run classifier with crossvalidation and plot ROC curves</span> <span class="n">cv</span> <span class="o">=</span> <span class="n">StratifiedKFold</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span> <span class="n">classifier</span> <span class="o">=</span> <span class="n">svm</span><span class="o">.</span><span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s">'linear'</span><span class="p">,</span> <span class="n">probability</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span> <span class="n">mean_tpr</span> <span class="o">=</span> <span class="mf">0.0</span> <span class="n">mean_fpr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span> <span class="n">all_tpr</span> <span class="o">=</span> <span class="p">[]</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">cv</span><span class="p">):</span> <span class="n">probas_</span> <span class="o">=</span> <span class="n">classifier</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">train</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="n">train</span><span class="p">])</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">test</span><span class="p">])</span> <span class="c"># Compute ROC curve and area the curve</span> <span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y</span><span class="p">[</span><span class="n">test</span><span class="p">],</span> <span class="n">probas_</span><span class="p">[:,</span><span class="mi">1</span><span class="p">])</span> <span class="n">mean_tpr</span> <span class="o">+=</span> <span class="n">interp</span><span class="p">(</span><span class="n">mean_fpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">)</span> <span class="n">mean_tpr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span> <span class="n">roc_auc</span> <span class="o">=</span> <span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</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="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">'ROC fold </span><span class="si">%d</span><span class="s"> (area = </span><span class="si">%0.2f</span><span class="s">)'</span> <span class="o">%</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">roc_auc</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="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s">'--'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="p">(</span><span class="mf">0.6</span><span class="p">,</span><span class="mf">0.6</span><span class="p">,</span><span class="mf">0.6</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="s">'Luck'</span><span class="p">)</span> <span class="n">mean_tpr</span> <span class="o">/=</span> <span class="nb">len</span><span class="p">(</span><span class="n">cv</span><span class="p">)</span> <span class="n">mean_tpr</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="n">mean_auc</span> <span class="o">=</span> <span class="n">auc</span><span class="p">(</span><span class="n">mean_fpr</span><span class="p">,</span> <span class="n">mean_tpr</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="n">mean_fpr</span><span class="p">,</span> <span class="n">mean_tpr</span><span class="p">,</span> <span class="s">'k--'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">'Mean ROC (area = </span><span class="si">%0.2f</span><span class="s">)'</span> <span class="o">%</span> <span class="n">mean_auc</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> <span class="n">pl</span><span class="o">.</span><span class="n">xlim</span><span class="p">([</span><span class="o">-</span><span class="mf">0.05</span><span class="p">,</span><span class="mf">1.05</span><span class="p">])</span> <span class="n">pl</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="o">-</span><span class="mf">0.05</span><span class="p">,</span><span class="mf">1.05</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">'False Positive Rate'</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">'True Positive Rate'</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">'Receiver operating characteristic example'</span><span class="p">)</span> <span class="n">pl</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s">"lower right"</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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