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<h1 class="title"><a href="mllib-guide.html">MLlib</a> - PMML model export</h1>
<ul id="markdown-toc">
<li><a href="#mllib-supported-models" id="markdown-toc-mllib-supported-models">MLlib supported models</a></li>
<li><a href="#examples" id="markdown-toc-examples">Examples</a></li>
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<h2 id="mllib-supported-models">MLlib supported models</h2>
<p>MLlib supports model export to Predictive Model Markup Language (<a href="http://en.wikipedia.org/wiki/Predictive_Model_Markup_Language">PMML</a>).</p>
<p>The table below outlines the MLlib models that can be exported to PMML and their equivalent PMML model.</p>
<table class="table">
<thead>
<tr><th>MLlib model</th><th>PMML model</th></tr>
</thead>
<tbody>
<tr>
<td>KMeansModel</td><td>ClusteringModel</td>
</tr>
<tr>
<td>LinearRegressionModel</td><td>RegressionModel (functionName="regression")</td>
</tr>
<tr>
<td>RidgeRegressionModel</td><td>RegressionModel (functionName="regression")</td>
</tr>
<tr>
<td>LassoModel</td><td>RegressionModel (functionName="regression")</td>
</tr>
<tr>
<td>SVMModel</td><td>RegressionModel (functionName="classification" normalizationMethod="none")</td>
</tr>
<tr>
<td>Binary LogisticRegressionModel</td><td>RegressionModel (functionName="classification" normalizationMethod="logit")</td>
</tr>
</tbody>
</table>
<h2 id="examples">Examples</h2>
<div class="codetabs">
<div data-lang="scala">
<p>To export a supported <code>model</code> (see table above) to PMML, simply call <code>model.toPMML</code>.</p>
<p>Here a complete example of building a KMeansModel and print it out in PMML format:</p>
<div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.clustering.KMeans</span>
<span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vectors</span>
<span class="c1">// Load and parse the data</span>
<span class="k">val</span> <span class="n">data</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="o">(</span><span class="s">"data/mllib/kmeans_data.txt"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">parsedData</span> <span class="k">=</span> <span class="n">data</span><span class="o">.</span><span class="n">map</span><span class="o">(</span><span class="n">s</span> <span class="k">=></span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span><span class="n">s</span><span class="o">.</span><span class="n">split</span><span class="o">(</span><span class="sc">' '</span><span class="o">).</span><span class="n">map</span><span class="o">(</span><span class="k">_</span><span class="o">.</span><span class="n">toDouble</span><span class="o">))).</span><span class="n">cache</span><span class="o">()</span>
<span class="c1">// Cluster the data into two classes using KMeans</span>
<span class="k">val</span> <span class="n">numClusters</span> <span class="k">=</span> <span class="mi">2</span>
<span class="k">val</span> <span class="n">numIterations</span> <span class="k">=</span> <span class="mi">20</span>
<span class="k">val</span> <span class="n">clusters</span> <span class="k">=</span> <span class="nc">KMeans</span><span class="o">.</span><span class="n">train</span><span class="o">(</span><span class="n">parsedData</span><span class="o">,</span> <span class="n">numClusters</span><span class="o">,</span> <span class="n">numIterations</span><span class="o">)</span>
<span class="c1">// Export to PMML</span>
<span class="n">println</span><span class="o">(</span><span class="s">"PMML Model:\n"</span> <span class="o">+</span> <span class="n">clusters</span><span class="o">.</span><span class="n">toPMML</span><span class="o">)</span></code></pre></div>
<p>As well as exporting the PMML model to a String (<code>model.toPMML</code> as in the example above), you can export the PMML model to other formats:</p>
<div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="c1">// Export the model to a String in PMML format</span>
<span class="n">clusters</span><span class="o">.</span><span class="n">toPMML</span>
<span class="c1">// Export the model to a local file in PMML format</span>
<span class="n">clusters</span><span class="o">.</span><span class="n">toPMML</span><span class="o">(</span><span class="s">"/tmp/kmeans.xml"</span><span class="o">)</span>
<span class="c1">// Export the model to a directory on a distributed file system in PMML format</span>
<span class="n">clusters</span><span class="o">.</span><span class="n">toPMML</span><span class="o">(</span><span class="n">sc</span><span class="o">,</span><span class="s">"/tmp/kmeans"</span><span class="o">)</span>
<span class="c1">// Export the model to the OutputStream in PMML format</span>
<span class="n">clusters</span><span class="o">.</span><span class="n">toPMML</span><span class="o">(</span><span class="nc">System</span><span class="o">.</span><span class="n">out</span><span class="o">)</span></code></pre></div>
<p>For unsupported models, either you will not find a <code>.toPMML</code> method or an <code>IllegalArgumentException</code> will be thrown.</p>
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