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| <h1 class="title">PMML model export - RDD-based API</h1> |
| |
| |
| <ul id="markdown-toc"> |
| <li><a href="#sparkmllib-supported-models" id="markdown-toc-sparkmllib-supported-models">spark.mllib supported models</a></li> |
| <li><a href="#examples" id="markdown-toc-examples">Examples</a></li> |
| </ul> |
| |
| <h2 id="sparkmllib-supported-models">spark.mllib supported models</h2> |
| |
| <p><code class="highlighter-rouge">spark.mllib</code> 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 <code class="highlighter-rouge">spark.mllib</code> models that can be exported to PMML and their equivalent PMML model.</p> |
| |
| <table class="table"> |
| <thead> |
| <tr><th>spark.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 class="highlighter-rouge">model</code> (see table above) to PMML, simply call <code class="highlighter-rouge">model.toPMML</code>.</p> |
| |
| <p>As well as exporting the PMML model to a String (<code class="highlighter-rouge">model.toPMML</code> as in the example above), you can export the PMML model to other formats.</p> |
| |
| <p>Refer to the <a href="api/scala/org/apache/spark/mllib/clustering/KMeans.html"><code class="highlighter-rouge">KMeans</code> Scala docs</a> and <a href="api/scala/org/apache/spark/mllib/linalg/Vectors$.html"><code class="highlighter-rouge">Vectors</code> Scala docs</a> for details on the API.</p> |
| |
| <p>Here a complete example of building a KMeansModel and print it out in PMML format:</p> |
| <div class="highlight"><pre class="codehilite"><code><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="nv">data</span> <span class="k">=</span> <span class="nv">sc</span><span class="o">.</span><span class="py">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="nv">parsedData</span> <span class="k">=</span> <span class="nv">data</span><span class="o">.</span><span class="py">map</span><span class="o">(</span><span class="n">s</span> <span class="k">=></span> <span class="nv">Vectors</span><span class="o">.</span><span class="py">dense</span><span class="o">(</span><span class="nv">s</span><span class="o">.</span><span class="py">split</span><span class="o">(</span><span class="sc">' '</span><span class="o">).</span><span class="py">map</span><span class="o">(</span><span class="nv">_</span><span class="o">.</span><span class="py">toDouble</span><span class="o">))).</span><span class="py">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="nv">numClusters</span> <span class="k">=</span> <span class="mi">2</span> |
| <span class="k">val</span> <span class="nv">numIterations</span> <span class="k">=</span> <span class="mi">20</span> |
| <span class="k">val</span> <span class="nv">clusters</span> <span class="k">=</span> <span class="nv">KMeans</span><span class="o">.</span><span class="py">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 to a String in PMML format</span> |
| <span class="nf">println</span><span class="o">(</span><span class="n">s</span><span class="s">"PMML Model:\n ${clusters.toPMML}"</span><span class="o">)</span> |
| |
| <span class="c1">// Export the model to a local file in PMML format</span> |
| <span class="nv">clusters</span><span class="o">.</span><span class="py">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="nv">clusters</span><span class="o">.</span><span class="py">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="nv">clusters</span><span class="o">.</span><span class="py">toPMML</span><span class="o">(</span><span class="nv">System</span><span class="o">.</span><span class="py">out</span><span class="o">)</span></code></pre></div> |
| <div><small>Find full example code at "examples/src/main/scala/org/apache/spark/examples/mllib/PMMLModelExportExample.scala" in the Spark repo.</small></div> |
| |
| <p>For unsupported models, either you will not find a <code class="highlighter-rouge">.toPMML</code> method or an <code class="highlighter-rouge">IllegalArgumentException</code> will be thrown.</p> |
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