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<!DOCTYPE html>
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<title>Plotting Models — pgmpy 0.1.23 documentation</title>
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<section id="plotting-models">
<h1>Plotting Models<a class="headerlink" href="#plotting-models" title="Link to this heading">¶</a></h1>
<p>pgmpy offers a few different ways to plot the model structure.</p>
<ol class="arabic simple">
<li><p>Using <cite>pygraphviz</cite> (<a class="reference external" href="https://pygraphviz.github.io/">https://pygraphviz.github.io/</a>)</p></li>
<li><p>Using <cite>networkx.drawing</cite> module (<a class="reference external" href="https://networkx.org/documentation/stable/reference/drawing.html">https://networkx.org/documentation/stable/reference/drawing.html</a>)</p></li>
<li><p>Using <cite>daft</cite> (<a class="reference external" href="https://docs.daft-pgm.org/">https://docs.daft-pgm.org/</a>)</p></li>
</ol>
<section id="using-pygraphviz">
<h2>1. Using <cite>pygraphviz</cite><a class="headerlink" href="#using-pygraphviz" title="Link to this heading">¶</a></h2>
<p><cite>pygraphviz</cite> is a Python wrapper to Graphviz that has a lot for functionality
for graph visualization. pgmpy provides a method to create a pygraphviz object from
Bayesian Networks and DAGs that can then be plotted using graphviz.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Get an example model</span>
<span class="kn">from</span> <span class="nn">pgmpy.utils</span> <span class="kn">import</span> <span class="n">get_example_model</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">get_example_model</span><span class="p">(</span><span class="s2">"sachs"</span><span class="p">)</span>
<span class="c1"># Convert model into pygraphviz object</span>
<span class="n">model_graphviz</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">to_graphviz</span><span class="p">()</span>
<span class="c1"># Plot the model.</span>
<span class="n">model_graphviz</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="s2">"sachs.png"</span><span class="p">,</span> <span class="n">prog</span><span class="o">=</span><span class="s2">"dot"</span><span class="p">)</span>
<span class="c1"># Other file formats can also be specified.</span>
<span class="n">model_graphviz</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="s2">"sachs.pdf"</span><span class="p">,</span> <span class="n">prog</span><span class="o">=</span><span class="s2">"dot"</span><span class="p">)</span>
<span class="n">model_graphviz</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="s2">"sachs.svg"</span><span class="p">,</span> <span class="n">prog</span><span class="o">=</span><span class="s2">"dot"</span><span class="p">)</span>
</pre></div>
</div>
<p>The output <cite>sachs.png</cite> is shown below. Users can also tryout other layout methods supported by pygraphviz such as: <cite>neato</cite>, <cite>dot</cite>, <cite>twopi</cite>, <cite>circo</cite>, <cite>fdp</cite>, <cite>nop</cite>.</p>
<a class="reference internal image-reference" href="_images/sachs.png"><img alt="_images/sachs.png" src="_images/sachs.png" style="width: 434.25px; height: 404.25px;" />
</a>
</section>
<section id="using-daft">
<h2>2. Using <cite>daft</cite><a class="headerlink" href="#using-daft" title="Link to this heading">¶</a></h2>
<p>Daft is a python package that uses matplotlib to render high quality plots suitable for publications.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Get an example model</span>
<span class="kn">from</span> <span class="nn">pgmpy.utils</span> <span class="kn">import</span> <span class="n">get_example_model</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">get_example_model</span><span class="p">(</span><span class="s2">"sachs"</span><span class="p">)</span>
<span class="c1"># Get a daft object.</span>
<span class="n">model_daft</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">to_daft</span><span class="p">()</span>
<span class="c1"># To open the plot</span>
<span class="n">model_daft</span><span class="o">.</span><span class="n">render</span><span class="p">()</span>
<span class="c1"># Save the plot</span>
<span class="n">model_daft</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'sachs.png'</span><span class="p">)</span>
<span class="c1"># Daft provides plenty of options for customization. Please refer DAG.to_daft documentation and daft's documentation.</span>
<span class="n">model_daft_custom</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">to_daft</span><span class="p">(</span><span class="n">node_pos</span><span class="o">=</span><span class="s1">'shell'</span><span class="p">,</span>
<span class="n">pgm_params</span><span class="o">=</span><span class="p">{</span><span class="s1">'observed_style'</span><span class="p">:</span> <span class="s1">'shade'</span><span class="p">,</span> <span class="s1">'grid_unit'</span><span class="p">:</span> <span class="mi">3</span><span class="p">},</span>
<span class="n">edge_params</span><span class="o">=</span><span class="p">{(</span><span class="s1">'PKA'</span><span class="p">,</span> <span class="s1">'P38'</span><span class="p">):</span> <span class="p">{</span><span class="s1">'label'</span><span class="p">:</span> <span class="mi">2</span><span class="p">}},</span>
<span class="n">node_params</span><span class="o">=</span><span class="p">{</span><span class="s1">'Mek'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'shape'</span><span class="p">:</span> <span class="s1">'rectangle'</span><span class="p">}})</span>
</pre></div>
</div>
<p>The output of the two plots above.</p>
<img alt="_images/sachs_daft_plain.png" src="_images/sachs_daft_plain.png" />
<img alt="_images/sachs_daft_shell.png" src="_images/sachs_daft_shell.png" />
</section>
<section id="using-networkx-drawing">
<h2>3. Using <cite>networkx.drawing</cite><a class="headerlink" href="#using-networkx-drawing" title="Link to this heading">¶</a></h2>
<p>Lastly, as both <cite>pgmpy.models.BayesianNetwork</cite> and <cite>pgmpy.base.DAG</cite> inherit <cite>networkx.DiGraph</cite>, all of networkx’s drawing functionality can be directly used on both DAGs and Bayesian Networks.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="c1"># Get an example model</span>
<span class="kn">from</span> <span class="nn">pgmpy.utils</span> <span class="kn">import</span> <span class="n">get_example_model</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">get_example_model</span><span class="p">(</span><span class="s2">"sachs"</span><span class="p">)</span>
<span class="c1"># Plot the model</span>
<span class="n">nx</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">draw</span><span class="p">()</span>
</pre></div>
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