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author | Reynold Xin <rxin@apache.org> | 2015-09-17 22:11:21 +0000 |
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committer | Reynold Xin <rxin@apache.org> | 2015-09-17 22:11:21 +0000 |
commit | 6f57b0c45a7d1b6255067c6e9bc549baa491acac (patch) | |
tree | dbf7d7a7700e9e6bad3c8289ab831bc9c2c20d62 /site/docs/1.5.0/mllib-pmml-model-export.html | |
parent | ee9ffe89d608e7640a2487406b618d27e58026d6 (diff) | |
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diff --git a/site/docs/1.5.0/mllib-pmml-model-export.html b/site/docs/1.5.0/mllib-pmml-model-export.html new file mode 100644 index 000000000..f5e406dd3 --- /dev/null +++ b/site/docs/1.5.0/mllib-pmml-model-export.html @@ -0,0 +1,245 @@ +<!DOCTYPE html> +<!--[if lt IE 7]> <html class="no-js lt-ie9 lt-ie8 lt-ie7"> <![endif]--> +<!--[if IE 7]> <html class="no-js lt-ie9 lt-ie8"> <![endif]--> +<!--[if IE 8]> <html class="no-js lt-ie9"> <![endif]--> +<!--[if gt IE 8]><!--> <html class="no-js"> <!--<![endif]--> + <head> + <meta charset="utf-8"> + <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1"> + <title>PMML model export - MLlib - Spark 1.5.0 Documentation</title> + + + + + <link rel="stylesheet" href="css/bootstrap.min.css"> + <style> + body { + padding-top: 60px; + padding-bottom: 40px; + } + </style> + <meta name="viewport" content="width=device-width"> + <link rel="stylesheet" href="css/bootstrap-responsive.min.css"> + <link rel="stylesheet" href="css/main.css"> + + <script src="js/vendor/modernizr-2.6.1-respond-1.1.0.min.js"></script> + + <link rel="stylesheet" href="css/pygments-default.css"> + + + <!-- Google analytics script --> + <script type="text/javascript"> + var _gaq = _gaq || []; + _gaq.push(['_setAccount', 'UA-32518208-2']); + _gaq.push(['_trackPageview']); + + (function() { + var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; + ga.src = ('https:' == document.location.protocol ? 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supported models</a></li> + <li><a href="#examples">Examples</a></li> +</ul> + +<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> + + </div> + +</div> + + + </div> <!-- /container --> + + <script src="js/vendor/jquery-1.8.0.min.js"></script> + <script src="js/vendor/bootstrap.min.js"></script> + <script src="js/vendor/anchor.min.js"></script> + <script src="js/main.js"></script> + + <!-- MathJax Section --> + <script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + TeX: { equationNumbers: { autoNumber: "AMS" } } + }); + </script> + <script> + // Note that we load MathJax this way to work with local file (file://), HTTP and HTTPS. + // We could use "//cdn.mathjax...", but that won't support "file://". + (function(d, script) { + script = d.createElement('script'); + script.type = 'text/javascript'; + script.async = true; + script.onload = function(){ + MathJax.Hub.Config({ + tex2jax: { + inlineMath: 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