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<meta name="description" content="Scikit-learn defines a simple API for creating visualizations for machine learning. The key features of this API are to run calculations once and to have the flexibility to adjust the visualization..." />
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<section id="developing-with-the-plotting-api">
<span id="plotting-api"></span><h1>Developing with the Plotting API<a class="headerlink" href="#developing-with-the-plotting-api" title="Link to this heading">#</a></h1>
<p>Scikit-learn defines a simple API for creating visualizations for machine
learning. The key features of this API are to run calculations once and to have
the flexibility to adjust the visualizations after the fact. This section is
intended for developers who wish to develop or maintain plotting tools. For
usage, users should refer to the <a class="reference internal" href="../visualizations.html#visualizations"><span class="std std-ref">User Guide</span></a>.</p>
<section id="plotting-api-overview">
<h2>Plotting API Overview<a class="headerlink" href="#plotting-api-overview" title="Link to this heading">#</a></h2>
<p>This logic is encapsulated into a display object where the computed data is
stored and the plotting is done in a <code class="docutils literal notranslate"><span class="pre">plot</span></code> method. The display object’s
<code class="docutils literal notranslate"><span class="pre">__init__</span></code> method contains only the data needed to create the visualization.
The <code class="docutils literal notranslate"><span class="pre">plot</span></code> method takes in parameters that only have to do with visualization,
such as a matplotlib axes. The <code class="docutils literal notranslate"><span class="pre">plot</span></code> method will store the matplotlib artists
as attributes allowing for style adjustments through the display object. The
<code class="docutils literal notranslate"><span class="pre">Display</span></code> class should define one or both class methods: <code class="docutils literal notranslate"><span class="pre">from_estimator</span></code> and
<code class="docutils literal notranslate"><span class="pre">from_predictions</span></code>. These methods allow creating the <code class="docutils literal notranslate"><span class="pre">Display</span></code> object from
the estimator and some data or from the true and predicted values. After these
class methods create the display object with the computed values, then call the
display’s plot method. Note that the <code class="docutils literal notranslate"><span class="pre">plot</span></code> method defines attributes related
to matplotlib, such as the line artist. This allows for customizations after
calling the <code class="docutils literal notranslate"><span class="pre">plot</span></code> method.</p>
<p>For example, the <code class="docutils literal notranslate"><span class="pre">RocCurveDisplay</span></code> defines the following methods and
attributes:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">RocCurveDisplay</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</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">roc_auc</span><span class="p">,</span> <span class="n">estimator_name</span><span class="p">):</span>
<span class="o">...</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fpr</span> <span class="o">=</span> <span class="n">fpr</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tpr</span> <span class="o">=</span> <span class="n">tpr</span>
<span class="bp">self</span><span class="o">.</span><span class="n">roc_auc</span> <span class="o">=</span> <span class="n">roc_auc</span>
<span class="bp">self</span><span class="o">.</span><span class="n">estimator_name</span> <span class="o">=</span> <span class="n">estimator_name</span>
<span class="nd">@classmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">from_estimator</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">estimator</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="c1"># get the predictions</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">estimator</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="mi">1</span><span class="p">]</span>
<span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_predictions</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">estimator</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
<span class="nd">@classmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">from_predictions</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">estimator_name</span><span class="p">):</span>
<span class="c1"># do ROC computation from y and y_pred</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">roc_auc</span> <span class="o">=</span> <span class="o">...</span>
<span class="n">viz</span> <span class="o">=</span> <span class="n">RocCurveDisplay</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">roc_auc</span><span class="p">,</span> <span class="n">estimator_name</span><span class="p">)</span>
<span class="k">return</span> <span class="n">viz</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
<span class="k">def</span><span class="w"> </span><span class="nf">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="o">...</span>
<span class="bp">self</span><span class="o">.</span><span class="n">line_</span> <span class="o">=</span> <span class="o">...</span>
<span class="bp">self</span><span class="o">.</span><span class="n">ax_</span> <span class="o">=</span> <span class="n">ax</span>
<span class="bp">self</span><span class="o">.</span><span class="n">figure_</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">figure_</span>
</pre></div>
</div>
<p>Read more in <a class="reference internal" href="../auto_examples/miscellaneous/plot_roc_curve_visualization_api.html#sphx-glr-auto-examples-miscellaneous-plot-roc-curve-visualization-api-py"><span class="std std-ref">ROC Curve with Visualization API</span></a>
and the <a class="reference internal" href="../visualizations.html#visualizations"><span class="std std-ref">User Guide</span></a>.</p>
</section>
<section id="plotting-with-multiple-axes">
<h2>Plotting with Multiple Axes<a class="headerlink" href="#plotting-with-multiple-axes" title="Link to this heading">#</a></h2>
<p>Some of the plotting tools like
<a class="reference internal" href="../modules/generated/sklearn.inspection.PartialDependenceDisplay.html#sklearn.inspection.PartialDependenceDisplay.from_estimator" title="sklearn.inspection.PartialDependenceDisplay.from_estimator"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_estimator</span></code></a> and
<a class="reference internal" href="../modules/generated/sklearn.inspection.PartialDependenceDisplay.html#sklearn.inspection.PartialDependenceDisplay" title="sklearn.inspection.PartialDependenceDisplay"><code class="xref py py-class docutils literal notranslate"><span class="pre">PartialDependenceDisplay</span></code></a> support plotting on
multiple axes. Two different scenarios are supported:</p>
<p>1. If a list of axes is passed in, <code class="docutils literal notranslate"><span class="pre">plot</span></code> will check if the number of axes is
consistent with the number of axes it expects and then draws on those axes. 2.
If a single axes is passed in, that axes defines a space for multiple axes to
be placed. In this case, we suggest using matplotlib’s
<code class="docutils literal notranslate"><span class="pre">~matplotlib.gridspec.GridSpecFromSubplotSpec</span></code> to split up the space:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">matplotlib.gridspec</span><span class="w"> </span><span class="kn">import</span> <span class="n">GridSpecFromSubplotSpec</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
<span class="n">gs</span> <span class="o">=</span> <span class="n">GridSpecFromSubplotSpec</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">subplot_spec</span><span class="o">=</span><span class="n">ax</span><span class="o">.</span><span class="n">get_subplotspec</span><span class="p">())</span>
<span class="n">ax_top_left</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
<span class="n">ax_top_right</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs</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="n">ax_bottom</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="p">:])</span>
</pre></div>
</div>
<p>By default, the <code class="docutils literal notranslate"><span class="pre">ax</span></code> keyword in <code class="docutils literal notranslate"><span class="pre">plot</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>. In this case, the single
axes is created and the gridspec api is used to create the regions to plot in.</p>
<p>See for example, <a class="reference internal" href="../modules/generated/sklearn.inspection.PartialDependenceDisplay.html#sklearn.inspection.PartialDependenceDisplay.from_estimator" title="sklearn.inspection.PartialDependenceDisplay.from_estimator"><code class="xref py py-meth docutils literal notranslate"><span class="pre">from_estimator</span></code></a>
which plots multiple lines and contours using this API. The axes defining the
bounding box are saved in a <code class="docutils literal notranslate"><span class="pre">bounding_ax_</span></code> attribute. The individual axes
created are stored in an <code class="docutils literal notranslate"><span class="pre">axes_</span></code> ndarray, corresponding to the axes position on
the grid. Positions that are not used are set to <code class="docutils literal notranslate"><span class="pre">None</span></code>. Furthermore, the
matplotlib Artists are stored in <code class="docutils literal notranslate"><span class="pre">lines_</span></code> and <code class="docutils literal notranslate"><span class="pre">contours_</span></code> where the key is the
position on the grid. When a list of axes is passed in, the <code class="docutils literal notranslate"><span class="pre">axes_</span></code>, <code class="docutils literal notranslate"><span class="pre">lines_</span></code>,
and <code class="docutils literal notranslate"><span class="pre">contours_</span></code> are a 1d ndarray corresponding to the list of axes passed in.</p>
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