<!DOCTYPE html><html><head><title>DASE Components Explained (Vanilla)</title><meta charset="utf-8"/><meta content="IE=edge,chrome=1" http-equiv="X-UA-Compatible"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><meta class="swiftype" name="title" data-type="string" content="DASE Components Explained (Vanilla)"/><link rel="canonical" href="https://docs.prediction.io/templates/vanilla/dase/"/><link href="/images/favicon/normal-b330020a.png" rel="shortcut icon"/><link href="/images/favicon/apple-c0febcf2.png" rel="apple-touch-icon"/><link href="//fonts.googleapis.com/css?family=Open+Sans:300italic,400italic,600italic,700italic,800italic,400,300,600,700,800" rel="stylesheet"/><link href="//maxcdn.bootstrapcdn.com/font-awesome/4.2.0/css/font-awesome.min.css" rel="stylesheet"/><link href="/stylesheets/application-3598c7d7.css" rel="stylesheet" type="text/css"/><script src="//cdnjs.cloudflare.com/ajax/libs/html5shiv/3.7.2/html5shiv.min.js"></script><script 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id="page-title"><h1>DASE Components Explained (Vanilla)</h1></div></div><div id="table-of-content-wrapper"><h5>On this page</h5><aside id="table-of-contents"><ul> <li> <a href="#algorithm">Algorithm</a> </li> </ul> </aside><hr/><a id="edit-page-link" href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/templates/vanilla/dase.html.md.erb"><img src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div class="content-header hidden-sm hidden-xs"><div id="page-title"><h1>DASE Components Explained (Vanilla)</h1></div></div><div class="content"><p>PredictionIO&#39;s DASE architecture brings the separation-of-concerns design principle to predictive engine development. DASE stands for the following components of an engine:</p> <ul> <li><strong>D</strong>ata - includes Data Source and Data Preparator</li> <li><strong>A</strong>lgorithm(s)</li> <li><strong>S</strong>erving</li> <li><strong>E</strong>valuator</li> </ul> <p><p>Let&#39;s look at the code and see how you can customize the engine you built from the Vanilla Engine Template.</p><div class="alert-message note"><p>Evaluator will not be covered in this tutorial.</p></div></p><p>Before you use Vanilla template to develop your engine, it&#39;s recommended that you go through the DASE explanation of one of the other templates (e.g. <em>Recommemdation template</em>, <em>Classification template</em>) to see a concrete example of how the DASE components are used.</p><h2 id='algorithm' class='header-anchors'>Algorithm</h2><p>PredictionIO supports two types of algorithms:</p> <ul> <li><strong>P2LAlgorithm</strong>: trains a Model which does not contain RDD</li> <li><strong>PAlgorithm</strong>: trains a Model which contains RDD</li> </ul> <h3 id='p2lalgorithm' class='header-anchors'>P2LAlgorithm</h3><p>By default, the Algorithm of the Vanilla template trains a simple model which does not contain RDD, as you can see in Algorithm.scala:</p><div class="highlight scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
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<span class="k">class</span> <span class="nc">Model</span><span class="o">(</span><span class="k">val</span> <span class="n">mc</span><span class="k">:</span> <span class="kt">Int</span><span class="o">)</span> <span class="k">extends</span> <span class="nc">Serializable</span> <span class="o">{</span>
  <span class="k">override</span> <span class="k">def</span> <span class="n">toString</span> <span class="k">=</span> <span class="n">s</span><span class="s">"mc=${mc}"</span>
<span class="o">}</span>

</pre></td></tr></tbody></table> </div> <p>In this case, the <code>Algorithm</code> class extends <code>P2LAlgorithm</code>, as you can see in Algorithm.scala:</p><div class="highlight scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
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<span class="k">class</span> <span class="nc">Algorithm</span><span class="o">(</span><span class="k">val</span> <span class="n">ap</span><span class="k">:</span> <span class="kt">AlgorithmParams</span><span class="o">)</span>
  <span class="c1">// extends PAlgorithm if Model contains RDD[]
</span>  <span class="k">extends</span> <span class="n">P2LAlgorithm</span><span class="o">[</span><span class="kt">PreparedData</span>, <span class="kt">Model</span>, <span class="kt">Query</span>, <span class="kt">PredictedResult</span><span class="o">]</span> <span class="o">{</span>

  <span class="o">...</span>

  <span class="k">def</span> <span class="n">train</span><span class="o">(</span><span class="n">sc</span><span class="k">:</span> <span class="kt">SparkContext</span><span class="o">,</span> <span class="n">data</span><span class="k">:</span> <span class="kt">PreparedData</span><span class="o">)</span><span class="k">:</span> <span class="kt">Model</span> <span class="o">=</span> <span class="o">{</span>
    <span class="c1">// Simply count number of events
</span>    <span class="c1">// and multiple it by the algorithm parameter
</span>    <span class="c1">// and store the number as model
</span>    <span class="k">val</span> <span class="n">count</span> <span class="k">=</span> <span class="n">data</span><span class="o">.</span><span class="n">events</span><span class="o">.</span><span class="n">count</span><span class="o">().</span><span class="n">toInt</span> <span class="o">*</span> <span class="n">ap</span><span class="o">.</span><span class="n">mult</span>
    <span class="k">new</span> <span class="nc">Model</span><span class="o">(</span><span class="n">mc</span> <span class="k">=</span> <span class="n">count</span><span class="o">)</span>
  <span class="o">}</span>

  <span class="o">...</span>

<span class="o">}</span>

</pre></td></tr></tbody></table> </div> <p>For `P2LAlgorithm, the Model is automatically serialized and persisted by PredictionIO after training.</p><div class="alert-message note"><p>You may also refer to Classification engine template for another example of P2LAlgorithm.</p></div><h3 id='palgorithm' class='header-anchors'>PAlgorithm</h3><p><code>PAlgorithm</code> should be used when your Model contains RDD. The model produced by <code>PAlgorithm</code> is not persisted by default. To persist the model, you need to do the following:</p> <ul> <li>The Model class should extend the <code>IPersistentModel</code> trait and implement the <code>save()</code> method for saving the model. The trait <code>IPersistentModel</code> requires a type parameter which is the class type of algorithm parameter.</li> <li>Implement a Model factory object which extends the <code>IPersistentModelLoader</code> trait and implement the <code>apply()</code> for loading the model. The trait <code>IPersistentModelLoader</code> requires two type parameters which are the types of algorithm parameter and the model produced by the algorithm.</li> </ul> <p>For example, let&#39;s say we add a new field <code>mRdd</code> which is type of <code>RDD[Int]</code> to the Vanilla template&#39;s <code>Model</code> class. The <code>Model</code> class is modified as following:</p><div class="highlight scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
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<span class="k">class</span> <span class="nc">Model</span><span class="o">(</span>
  <span class="k">val</span> <span class="n">mc</span><span class="k">:</span> <span class="kt">Int</span><span class="o">,</span>
  <span class="k">val</span> <span class="n">mRdd</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">Int</span><span class="o">]</span> <span class="c1">// ADDED
</span>  <span class="o">)</span> <span class="k">extends</span> <span class="nc">IPersistentModel</span><span class="o">[</span><span class="kt">AlgorithmParams</span><span class="o">]</span> <span class="k">with</span> <span class="nc">Serializable</span> <span class="o">{</span> <span class="c1">// ADDED
</span>
    <span class="c1">// ADDED
</span>    <span class="k">def</span> <span class="n">save</span><span class="o">(</span><span class="n">id</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span> <span class="n">params</span><span class="k">:</span> <span class="kt">AlgorithmParams</span><span class="o">,</span>
      <span class="n">sc</span><span class="k">:</span> <span class="kt">SparkContext</span><span class="o">)</span><span class="k">:</span> <span class="kt">Boolean</span> <span class="o">=</span> <span class="o">{</span>

      <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="o">(</span><span class="nc">Seq</span><span class="o">(</span><span class="n">mc</span><span class="o">)).</span><span class="n">saveAsObjectFile</span><span class="o">(</span><span class="n">s</span><span class="s">"/tmp/${id}/mc"</span><span class="o">)</span>
      <span class="n">mRdd</span><span class="o">.</span><span class="n">saveAsObjectFile</span><span class="o">(</span><span class="n">s</span><span class="s">"/tmp/${id}/mRdd"</span><span class="o">)</span>
      <span class="kc">true</span>
    <span class="o">}</span>

    <span class="k">override</span>  <span class="k">def</span> <span class="n">toString</span> <span class="k">=</span> <span class="o">{</span>
      <span class="n">s</span><span class="s">"mc=${mc}"</span> <span class="o">+</span>
      <span class="c1">// ADDED for debugging
</span>      <span class="n">s</span><span class="s">"mRdd=[${mRdd.count()}] (${mRdd.take(2).toList}...)"</span>
    <span class="o">}</span>
<span class="o">}</span>

</pre></td></tr></tbody></table> </div> <p>Notice that it extends <code>IPersistentModel[AlgorithmParams]</code> and implement the <code>save()</code> method.</p><p>Next, we need to implement a Model factory object to load back the persisted model and return the Model instance:</p><div class="highlight scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
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<span class="c1">// ADDED
</span><span class="k">object</span> <span class="nc">Model</span>
  <span class="k">extends</span> <span class="nc">IPersistentModelLoader</span><span class="o">[</span><span class="kt">AlgorithmParams</span>, <span class="kt">Model</span><span class="o">]</span> <span class="o">{</span>
  <span class="k">def</span> <span class="n">apply</span><span class="o">(</span><span class="n">id</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span> <span class="n">params</span><span class="k">:</span> <span class="kt">AlgorithmParams</span><span class="o">,</span>
    <span class="n">sc</span><span class="k">:</span> <span class="kt">Option</span><span class="o">[</span><span class="kt">SparkContext</span><span class="o">])</span> <span class="k">=</span> <span class="o">{</span>
    <span class="k">new</span> <span class="nc">Model</span><span class="o">(</span>
      <span class="n">mc</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">get</span><span class="o">.</span><span class="n">objectFile</span><span class="o">[</span><span class="kt">Int</span><span class="o">](</span><span class="n">s</span><span class="s">"/tmp/${id}/mc"</span><span class="o">).</span><span class="n">first</span><span class="o">,</span>
      <span class="n">mRdd</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">get</span><span class="o">.</span><span class="n">objectFile</span><span class="o">(</span><span class="n">s</span><span class="s">"/tmp/${id}/mRdd"</span><span class="o">)</span>
    <span class="o">)</span>
  <span class="o">}</span>
<span class="o">}</span>

</pre></td></tr></tbody></table> </div> <p>At last, the <code>Algorithm</code> class needs to extend <code>PAlgorithm</code> and generate the RDD data for the new <code>mRdd</code> field in <code>Model</code>:</p><div class="highlight scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
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<span class="k">class</span> <span class="nc">Algorithm</span><span class="o">(</span><span class="k">val</span> <span class="n">ap</span><span class="k">:</span> <span class="kt">AlgorithmParams</span><span class="o">)</span>
  <span class="k">extends</span> <span class="nc">PAlgorithm</span><span class="o">[</span><span class="kt">PreparedData</span>, <span class="kt">Model</span>, <span class="kt">Query</span>, <span class="kt">PredictedResult</span><span class="o">]</span> <span class="o">{</span> <span class="c1">// MODIFIED
</span>
  <span class="o">...</span>

  <span class="k">def</span> <span class="n">train</span><span class="o">(</span><span class="n">sc</span><span class="k">:</span> <span class="kt">SparkContext</span><span class="o">,</span> <span class="n">data</span><span class="k">:</span> <span class="kt">PreparedData</span><span class="o">)</span><span class="k">:</span> <span class="kt">Model</span> <span class="o">=</span> <span class="o">{</span>
    <span class="c1">// Simply count number of events
</span>    <span class="c1">// and multiple it by the algorithm parameter
</span>    <span class="c1">// and store the number as model
</span>    <span class="k">val</span> <span class="n">count</span> <span class="k">=</span> <span class="n">data</span><span class="o">.</span><span class="n">events</span><span class="o">.</span><span class="n">count</span><span class="o">().</span><span class="n">toInt</span> <span class="o">*</span> <span class="n">ap</span><span class="o">.</span><span class="n">mult</span>

    <span class="c1">// ADDED
</span>    <span class="c1">// get the spark context
</span>    <span class="k">val</span> <span class="n">sc</span> <span class="k">=</span> <span class="n">data</span><span class="o">.</span><span class="n">events</span><span class="o">.</span><span class="n">context</span>
    <span class="c1">// create dummy RDD[Int] for demonstration purpose
</span>    <span class="k">val</span> <span class="n">mRdd</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="o">(</span><span class="nc">Seq</span><span class="o">(</span><span class="mi">1</span><span class="o">,</span><span class="mi">2</span><span class="o">,</span><span class="mi">3</span><span class="o">))</span>

    <span class="k">new</span> <span class="nc">Model</span><span class="o">(</span>
      <span class="n">mc</span> <span class="k">=</span> <span class="n">count</span><span class="o">,</span>
      <span class="n">mRdd</span> <span class="k">=</span> <span class="n">mRdd</span> <span class="c1">// ADDED
</span>    <span class="o">)</span>
  <span class="o">}</span>

  <span class="o">...</span>

<span class="o">}</span>

</pre></td></tr></tbody></table> </div> <div class="alert-message note"><p>You may also refer to Similar Product engine template for another example of PAlgorithm.</p></div></div></div></div></div><footer><div class="container"><div class="seperator"></div><div class="row"><div class="col-md-6 col-xs-6 footer-link-column"><div class="footer-link-column-row"><h4>Community</h4><ul><li><a href="//docs.prediction.io/install/" target="blank">Download</a></li><li><a href="//docs.prediction.io/" target="blank">Docs</a></li><li><a href="//github.com/apache/incubator-predictionio" target="blank">GitHub</a></li><li><a href="mailto:user-subscribe@predictionio.incubator.apache.org" target="blank">Subscribe to User Mailing List</a></li><li><a href="//stackoverflow.com/questions/tagged/predictionio" target="blank">Stackoverflow</a></li></ul></div></div><div class="col-md-6 col-xs-6 footer-link-column"><div class="footer-link-column-row"><h4>Contribute</h4><ul><li><a href="//predictionio.incubator.apache.org/community/contribute-code/" target="blank">Contribute</a></li><li><a href="//github.com/apache/incubator-predictionio" target="blank">Source Code</a></li><li><a href="//issues.apache.org/jira/browse/PIO" target="blank">Bug Tracker</a></li><li><a href="mailto:dev-subscribe@predictionio.incubator.apache.org" target="blank">Subscribe to Development Mailing List</a></li></ul></div></div></div></div><div id="footer-bottom"><div class="container"><div class="row"><div class="col-md-12"><div id="footer-logo-wrapper"><img alt="PredictionIO" src="/images/logos/logo-white-d1e9c6e6.png"/></div><div id="social-icons-wrapper"><a class="github-button" href="https://github.com/apache/incubator-predictionio" data-style="mega" data-count-href="/apache/incubator-predictionio/stargazers" data-count-api="/repos/apache/incubator-predictionio#stargazers_count" data-count-aria-label="# stargazers on GitHub" aria-label="Star apache/incubator-predictionio on GitHub">Star</a> <a class="github-button" href="https://github.com/apache/incubator-predictionio/fork" data-icon="octicon-git-branch" data-style="mega" data-count-href="/apache/incubator-predictionio/network" data-count-api="/repos/apache/incubator-predictionio#forks_count" data-count-aria-label="# forks on GitHub" aria-label="Fork apache/incubator-predictionio on GitHub">Fork</a> <script id="github-bjs" async="" defer="" src="https://buttons.github.io/buttons.js"></script><a href="//www.facebook.com/predictionio" target="blank"><img alt="PredictionIO on Twitter" src="/images/icons/twitter-ea9dc152.png"/></a> <a href="//twitter.com/predictionio" target="blank"><img alt="PredictionIO on Facebook" src="/images/icons/facebook-5c57939c.png"/></a> </div></div></div></div></div></footer></div><script>(function(w,d,t,u,n,s,e){w['SwiftypeObject']=n;w[n]=w[n]||function(){
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