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<li><a class="reference internal" href="#">Developing with the Plotting API</a><ul>
<li><a class="reference internal" href="#plotting-api-overview">Plotting API Overview</a></li>
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<div class="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="Permalink to this headline">¶</a></h1>
<p>Scikit-learn defines a simple API for creating visualizations for machine
learning. The key features of this API is 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 :ref`User Guide <visualizations>`.</p>
<div class="section" id="plotting-api-overview">
<h2>Plotting API Overview<a class="headerlink" href="#plotting-api-overview" title="Permalink to this headline">¶</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. A
<code class="docutils literal notranslate"><span class="pre">plot_*</span></code> helper function accepts parameters to do the computation and the
parameters used for plotting. After the helper function creates the display
object with the computed values, it calls 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="nc">RocCurveDisplay</span><span class="p">:</span>
<span class="k">def</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="k">def</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>
<span class="k">def</span> <span class="nf">plot_roc_curve</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="n">pos_label</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">drop_intermediate</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">response_method</span><span class="o">=</span><span class="s2">"auto"</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="n">ax</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="c1"># do computation</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</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="k">return</span> <span class="n">viz</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</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>
</div>
<div class="section" id="plotting-with-multiple-axes">
<h2>Plotting with Multiple Axes<a class="headerlink" href="#plotting-with-multiple-axes" title="Permalink to this headline">¶</a></h2>
<p>Some of the plotting tools like
<a class="reference internal" href="../modules/generated/sklearn.inspection.plot_partial_dependence.html#sklearn.inspection.plot_partial_dependence" title="sklearn.inspection.plot_partial_dependence"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_partial_dependence</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 plottong 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="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">matplotlib.gridspec</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.plot_partial_dependence.html#sklearn.inspection.plot_partial_dependence" title="sklearn.inspection.plot_partial_dependence"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_partial_dependence</span></code></a> which
plots multiple lines and contours using this API. The axes defining the
bounding box is 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> is a 1d ndarray corresponding to the list of axes passed in.</p>
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