<!DOCTYPE html><html><head><title>DASE Components Explained (Recommendation)</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 (Recommendation)"/><link rel="canonical" href="https://docs.prediction.io/templates/recommendation/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 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hidden-lg"><div id="page-title"><h1>DASE Components Explained (Recommendation)</h1></div></div><div id="table-of-content-wrapper"><h5>On this page</h5><aside id="table-of-contents"><ul> <li> <a href="#the-engine-design">The Engine Design</a> </li> <li> <a href="#data">Data</a> </li> <li> <a href="#algorithm">Algorithm</a> </li> <li> <a href="#serving">Serving</a> </li> </ul> </aside><hr/><a id="edit-page-link" href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/templates/recommendation/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 (Recommendation)</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 Recommendation Engine Template.</p><div class="alert-message note"><p>Evaluator will not be covered in this tutorial. Please visit <a href="/templates/recommendation/evaluation/">evaluation explained</a> for using evaluation.</p></div></p><h2 id='the-engine-design' class='header-anchors'>The Engine Design</h2><p>As you can see from the Quick Start, <em>MyRecommendation</em> takes a JSON prediction query, e.g. <code>{ &quot;user&quot;: &quot;1&quot;, &quot;num&quot;: 4 }</code>, and return a JSON predicted result. In MyRecommendation/src/main/scala/<strong><em>Engine.scala</em></strong>, the <code>Query</code> case class defines the format of such <strong>query</strong>:</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="n">user</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span>
  <span class="n">num</span><span class="k">:</span> <span class="kt">Int</span>
<span class="o">)</span> <span class="k">extends</span> <span class="nc">Serializable</span>
</pre></td></tr></tbody></table> </div> <p>The <code>PredictedResult</code> case class defines the format of <strong>predicted result</strong>, such as</p><div class="highlight json"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
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  </span><span class="p">{</span><span class="s2">"item"</span><span class="p">:</span><span class="mi">22</span><span class="p">,</span><span class="s2">"score"</span><span class="p">:</span><span class="mf">4.07</span><span class="p">},</span><span class="w">
  </span><span class="p">{</span><span class="s2">"item"</span><span class="p">:</span><span class="mi">62</span><span class="p">,</span><span class="s2">"score"</span><span class="p">:</span><span class="mf">4.05</span><span class="p">},</span><span class="w">
  </span><span class="p">{</span><span class="s2">"item"</span><span class="p">:</span><span class="mi">75</span><span class="p">,</span><span class="s2">"score"</span><span class="p">:</span><span class="mf">4.04</span><span class="p">},</span><span class="w">
  </span><span class="p">{</span><span class="s2">"item"</span><span class="p">:</span><span class="mi">68</span><span class="p">,</span><span class="s2">"score"</span><span class="p">:</span><span class="mf">3.81</span><span class="p">}</span><span class="w">
</span><span class="p">]}</span><span class="w">
</span></pre></td></tr></tbody></table> </div> <p>with:</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="n">itemScores</span><span class="k">:</span> <span class="kt">Array</span><span class="o">[</span><span class="kt">ItemScore</span><span class="o">]</span>
<span class="o">)</span> <span class="k">extends</span> <span class="nc">Serializable</span>

<span class="k">case</span> <span class="k">class</span> <span class="nc">ItemScore</span><span class="o">(</span>
  <span class="n">item</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span>
  <span class="n">score</span><span class="k">:</span> <span class="kt">Double</span>
<span class="o">)</span> <span class="k">extends</span> <span class="nc">Serializable</span>
</pre></td></tr></tbody></table> </div> <p>Finally, <code>RecommendationEngine</code> is the <em>Engine Factory</em> that defines the components this engine will use: Data Source, Data Preparator, Algorithm(s) and Serving components.</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">def</span> <span class="n">apply</span><span class="o">()</span> <span class="k">=</span> <span class="o">{</span>
    <span class="k">new</span> <span class="nc">Engine</span><span class="o">(</span>
      <span class="n">classOf</span><span class="o">[</span><span class="kt">DataSource</span><span class="o">],</span>
      <span class="n">classOf</span><span class="o">[</span><span class="kt">Preparator</span><span class="o">],</span>
      <span class="nc">Map</span><span class="o">(</span><span class="s">"als"</span> <span class="o">-&gt;</span> <span class="n">classOf</span><span class="o">[</span><span class="kt">ALSAlgorithm</span><span class="o">]),</span>
      <span class="n">classOf</span><span class="o">[</span><span class="kt">Serving</span><span class="o">])</span>
  <span class="o">}</span>
  <span class="o">...</span>
<span class="o">}</span>
</pre></td></tr></tbody></table> </div> <h3 id='spark-mllib' class='header-anchors'>Spark MLlib</h3><p>Spark&#39;s MLlib ALS algorithm takes training data of RDD type, i.e. <code>RDD[Rating]</code> and train a model, which is a <code>MatrixFactorizationModel</code> object.</p><p>PredictionIO Recommendation Engine Template, which <em>MyRecommendation</em> bases on, integrates this algorithm under the DASE architecture. We will take a closer look at the DASE code below.</p><div class="alert-message info"><p><a href="https://spark.apache.org/docs/latest/mllib-collaborative-filtering.html">Check this out</a> to learn more about MLlib&#39;s ALS collaborative filtering algorithm.</p></div><h2 id='data' class='header-anchors'>Data</h2><p>In the DASE architecture, data is prepared by 2 components sequentially: <em>Data Source</em> and <em>Data Preparator</em>. <em>Data Source</em> and <em>Data Preparator</em> takes data from the data store and prepares <code>RDD[Rating]</code> for the ALS algorithm.</p><h3 id='data-source' class='header-anchors'>Data Source</h3><p>In MyRecommendation/src/main/scala/<strong><em>DataSource.scala</em></strong>, the <code>readTraining</code> method of class <code>DataSource</code> reads, and selects, data from the <em>Event Store</em> (data store of the <em>Event Server</em>) and returns <code>TrainingData</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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46</pre></td><td class="code"><pre><span class="k">case</span> <span class="k">class</span> <span class="nc">DataSourceParams</span><span class="o">(</span><span class="n">appName</span><span class="k">:</span> <span class="kt">String</span><span class="o">)</span> <span class="k">extends</span> <span class="nc">Params</span>

<span class="k">class</span> <span class="nc">DataSource</span><span class="o">(</span><span class="k">val</span> <span class="n">dsp</span><span class="k">:</span> <span class="kt">DataSourceParams</span><span class="o">)</span>
  <span class="k">extends</span> <span class="nc">PDataSource</span><span class="o">[</span><span class="kt">TrainingData</span>,
      <span class="kt">EmptyEvaluationInfo</span>, <span class="kt">Query</span>, <span class="kt">EmptyActualResult</span><span class="o">]</span> <span class="o">{</span>

  <span class="nd">@transient</span> <span class="k">lazy</span> <span class="k">val</span> <span class="n">logger</span> <span class="k">=</span> <span class="nc">Logger</span><span class="o">[</span><span class="kt">this.</span><span class="k">type</span><span class="o">]</span>

  <span class="k">def</span> <span class="n">getRatings</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">RDD</span><span class="o">[</span><span class="kt">Rating</span><span class="o">]</span> <span class="k">=</span> <span class="o">{</span>

    <span class="k">val</span> <span class="n">eventsRDD</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">Event</span><span class="o">]</span> <span class="k">=</span> <span class="nc">PEventStore</span><span class="o">.</span><span class="n">find</span><span class="o">(</span>
      <span class="n">appName</span> <span class="k">=</span> <span class="n">dsp</span><span class="o">.</span><span class="n">appName</span><span class="o">,</span>
      <span class="n">entityType</span> <span class="k">=</span> <span class="nc">Some</span><span class="o">(</span><span class="s">"user"</span><span class="o">),</span>
      <span class="n">eventNames</span> <span class="k">=</span> <span class="nc">Some</span><span class="o">(</span><span class="nc">List</span><span class="o">(</span><span class="s">"rate"</span><span class="o">,</span> <span class="s">"buy"</span><span class="o">)),</span> <span class="c1">// read "rate" and "buy" event
</span>      <span class="c1">// targetEntityType is optional field of an event.
</span>      <span class="n">targetEntityType</span> <span class="k">=</span> <span class="nc">Some</span><span class="o">(</span><span class="nc">Some</span><span class="o">(</span><span class="s">"item"</span><span class="o">)))(</span><span class="n">sc</span><span class="o">)</span>

    <span class="k">val</span> <span class="n">ratingsRDD</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">Rating</span><span class="o">]</span> <span class="k">=</span> <span class="n">eventsRDD</span><span class="o">.</span><span class="n">map</span> <span class="o">{</span> <span class="n">event</span> <span class="k">=&gt;</span>
      <span class="k">val</span> <span class="n">rating</span> <span class="k">=</span> <span class="k">try</span> <span class="o">{</span>
        <span class="k">val</span> <span class="n">ratingValue</span><span class="k">:</span> <span class="kt">Double</span> <span class="o">=</span> <span class="n">event</span><span class="o">.</span><span class="n">event</span> <span class="k">match</span> <span class="o">{</span>
          <span class="k">case</span> <span class="s">"rate"</span> <span class="k">=&gt;</span> <span class="n">event</span><span class="o">.</span><span class="n">properties</span><span class="o">.</span><span class="n">get</span><span class="o">[</span><span class="kt">Double</span><span class="o">](</span><span class="s">"rating"</span><span class="o">)</span>
          <span class="k">case</span> <span class="s">"buy"</span> <span class="k">=&gt;</span> <span class="mf">4.0</span> <span class="c1">// map buy event to rating value of 4
</span>          <span class="k">case</span> <span class="k">_</span> <span class="k">=&gt;</span> <span class="k">throw</span> <span class="k">new</span> <span class="nc">Exception</span><span class="o">(</span><span class="n">s</span><span class="s">"Unexpected event ${event} is read."</span><span class="o">)</span>
        <span class="o">}</span>
        <span class="c1">// entityId and targetEntityId is String
</span>        <span class="nc">Rating</span><span class="o">(</span><span class="n">event</span><span class="o">.</span><span class="n">entityId</span><span class="o">,</span>
          <span class="n">event</span><span class="o">.</span><span class="n">targetEntityId</span><span class="o">.</span><span class="n">get</span><span class="o">,</span>
          <span class="n">ratingValue</span><span class="o">)</span>
      <span class="o">}</span> <span class="k">catch</span> <span class="o">{</span>
        <span class="k">case</span> <span class="n">e</span><span class="k">:</span> <span class="kt">Exception</span> <span class="o">=&gt;</span> <span class="o">{</span>
          <span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="o">(</span><span class="n">s</span><span class="s">"Cannot convert ${event} to Rating. Exception: ${e}."</span><span class="o">)</span>
          <span class="k">throw</span> <span class="n">e</span>
        <span class="o">}</span>
      <span class="o">}</span>
      <span class="n">rating</span>
    <span class="o">}.</span><span class="n">cache</span><span class="o">()</span>

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

  <span class="k">override</span>
  <span class="k">def</span> <span class="n">readTraining</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">TrainingData</span> <span class="o">=</span> <span class="o">{</span>
    <span class="k">new</span> <span class="nc">TrainingData</span><span class="o">(</span><span class="n">getRatings</span><span class="o">(</span><span class="n">sc</span><span class="o">))</span>
  <span class="o">}</span>

<span class="o">}</span>
</pre></td></tr></tbody></table> </div> <p><code>PEventStore</code> is an object which provides function to access data that is collected by PredictionIO <em>Event Server</em>. <code>PEventStore.find(...)</code> specifies the events that you want to read. PredictionIO automatically loads the parameters of <em>datasource</em> specified in MyRecommendation/<strong><em>engine.json</em></strong>, including <em>appName</em>, to <code>dsp</code>.</p><p>In <strong><em>engine.json</em></strong>:</p><div class="highlight shell"><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="s2">"datasource"</span>: <span class="o">{</span>
    <span class="s2">"params"</span> : <span class="o">{</span>
      <span class="s2">"appName"</span>: <span class="s2">"MyApp1"</span>
    <span class="o">}</span>
  <span class="o">}</span>,
  ...
<span class="o">}</span>
</pre></td></tr></tbody></table> </div> <p>Each <em>rate</em> and <em>buy</em> user event data is read as <code>Rating</code>. For flexibility, this Recommendation engine template is designed to support user ID and item ID in <code>String</code>. Since Spark MLlib&#39;s <code>Rating</code> class assumes <code>Int</code>-only user ID and item ID, you have to define a new <code>Rating</code> class:</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="n">user</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span>
  <span class="n">item</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span>
  <span class="n">rating</span><span class="k">:</span> <span class="kt">Double</span>
<span class="o">)</span>
</pre></td></tr></tbody></table> </div> <p><code>TrainingData</code> contains an RDD of all these <code>Rating</code> events. The class definition of <code>TrainingData</code> is:</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">val</span> <span class="n">ratings</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">Rating</span><span class="o">]</span>
<span class="o">)</span> <span class="k">extends</span> <span class="nc">Serializable</span> <span class="o">{...}</span>
</pre></td></tr></tbody></table> </div> <p>and PredictionIO passes the returned <code>TrainingData</code> object to <em>Data Preparator</em>.</p> <div class="alert-message info"><p>You could <a href="/templates/recommendation/reading-custom-events/">modify the DataSource to read custom events</a> other than the default <strong>rate</strong> and <strong>buy</strong>.</p></div><h3 id='data-preparator' class='header-anchors'>Data Preparator</h3><p>In MyRecommendation/src/main/scala/<strong><em>Preparator.scala</em></strong>, the <code>prepare</code> method of class <code>Preparator</code> takes <code>TrainingData</code> as its input and performs any necessary feature selection and data processing tasks. At the end, it returns <code>PreparedData</code> which should contain the data <em>Algorithm</em> needs. For MLlib ALS, it is <code>RDD[Rating]</code>.</p><p>By default, <code>prepare</code> simply copies the unprocessed <code>TrainingData</code> data to <code>PreparedData</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">extends</span> <span class="nc">PPreparator</span><span class="o">[</span><span class="kt">TrainingData</span>, <span class="kt">PreparedData</span><span class="o">]</span> <span class="o">{</span>

  <span class="k">def</span> <span class="n">prepare</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">trainingData</span><span class="k">:</span> <span class="kt">TrainingData</span><span class="o">)</span><span class="k">:</span> <span class="kt">PreparedData</span> <span class="o">=</span> <span class="o">{</span>
    <span class="k">new</span> <span class="nc">PreparedData</span><span class="o">(</span><span class="n">ratings</span> <span class="k">=</span> <span class="n">trainingData</span><span class="o">.</span><span class="n">ratings</span><span class="o">)</span>
  <span class="o">}</span>
<span class="o">}</span>

<span class="k">class</span> <span class="nc">PreparedData</span><span class="o">(</span>
  <span class="k">val</span> <span class="n">ratings</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">Rating</span><span class="o">]</span>
<span class="o">)</span> <span class="k">extends</span> <span class="nc">Serializable</span>
</pre></td></tr></tbody></table> </div> <p>PredictionIO passes the returned <code>PreparedData</code> object to Algorithm&#39;s <code>train</code> function.</p> <h2 id='algorithm' class='header-anchors'>Algorithm</h2><p>In MyRecommendation/src/main/scala/<strong><em>ALSAlgorithm.scala</em></strong>, the two methods of the algorithm class are <code>train</code> and <code>predict</code>. <code>train</code> is responsible for training a predictive model. PredictionIO will store this model and <code>predict</code> is responsible for using this model to make prediction.</p><h3 id='train(...)' class='header-anchors'>train(...)</h3><p><code>train</code> is called when you run <strong>pio train</strong>. This is where MLlib ALS algorithm, i.e. <code>ALS.train</code>, is used to train a predictive model.</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="o">...</span>
    <span class="c1">// Convert user and item String IDs to Int index for MLlib
</span>    <span class="k">val</span> <span class="n">userStringIntMap</span> <span class="k">=</span> <span class="nc">BiMap</span><span class="o">.</span><span class="n">stringInt</span><span class="o">(</span><span class="n">data</span><span class="o">.</span><span class="n">ratings</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">user</span><span class="o">))</span>
    <span class="k">val</span> <span class="n">itemStringIntMap</span> <span class="k">=</span> <span class="nc">BiMap</span><span class="o">.</span><span class="n">stringInt</span><span class="o">(</span><span class="n">data</span><span class="o">.</span><span class="n">ratings</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">item</span><span class="o">))</span>
    <span class="k">val</span> <span class="n">mllibRatings</span> <span class="k">=</span> <span class="n">data</span><span class="o">.</span><span class="n">ratings</span><span class="o">.</span><span class="n">map</span><span class="o">(</span> <span class="n">r</span> <span class="k">=&gt;</span>
      <span class="c1">// MLlibRating requires integer index for user and item
</span>      <span class="nc">MLlibRating</span><span class="o">(</span><span class="n">userStringIntMap</span><span class="o">(</span><span class="n">r</span><span class="o">.</span><span class="n">user</span><span class="o">),</span> <span class="n">itemStringIntMap</span><span class="o">(</span><span class="n">r</span><span class="o">.</span><span class="n">item</span><span class="o">),</span> <span class="n">r</span><span class="o">.</span><span class="n">rating</span><span class="o">)</span>
    <span class="o">)</span>

    <span class="c1">// seed for MLlib ALS
</span>    <span class="k">val</span> <span class="n">seed</span> <span class="k">=</span> <span class="n">ap</span><span class="o">.</span><span class="n">seed</span><span class="o">.</span><span class="n">getOrElse</span><span class="o">(</span><span class="nc">System</span><span class="o">.</span><span class="n">nanoTime</span><span class="o">)</span>

    <span class="c1">// If you only have one type of implicit event (Eg. "view" event only),
</span>    <span class="c1">// replace ALS.train(...) with
</span>    <span class="c1">//val m = ALS.trainImplicit(
</span>      <span class="c1">//ratings = mllibRatings,
</span>      <span class="c1">//rank = ap.rank,
</span>      <span class="c1">//iterations = ap.numIterations,
</span>      <span class="c1">//lambda = ap.lambda,
</span>      <span class="c1">//blocks = -1,
</span>      <span class="c1">//alpha = 1.0,
</span>      <span class="c1">//seed = seed)
</span>
    <span class="k">val</span> <span class="n">m</span> <span class="k">=</span> <span class="nc">ALS</span><span class="o">.</span><span class="n">train</span><span class="o">(</span>
      <span class="n">ratings</span> <span class="k">=</span> <span class="n">mllibRatings</span><span class="o">,</span>
      <span class="n">rank</span> <span class="k">=</span> <span class="n">ap</span><span class="o">.</span><span class="n">rank</span><span class="o">,</span>
      <span class="n">iterations</span> <span class="k">=</span> <span class="n">ap</span><span class="o">.</span><span class="n">numIterations</span><span class="o">,</span>
      <span class="n">lambda</span> <span class="k">=</span> <span class="n">ap</span><span class="o">.</span><span class="n">lambda</span><span class="o">,</span>
      <span class="n">blocks</span> <span class="k">=</span> <span class="o">-</span><span class="mi">1</span><span class="o">,</span>
      <span class="n">seed</span> <span class="k">=</span> <span class="n">seed</span><span class="o">)</span>

    <span class="k">new</span> <span class="nc">ALSModel</span><span class="o">(</span>
      <span class="n">rank</span> <span class="k">=</span> <span class="n">m</span><span class="o">.</span><span class="n">rank</span><span class="o">,</span>
      <span class="n">userFeatures</span> <span class="k">=</span> <span class="n">m</span><span class="o">.</span><span class="n">userFeatures</span><span class="o">,</span>
      <span class="n">productFeatures</span> <span class="k">=</span> <span class="n">m</span><span class="o">.</span><span class="n">productFeatures</span><span class="o">,</span>
      <span class="n">userStringIntMap</span> <span class="k">=</span> <span class="n">userStringIntMap</span><span class="o">,</span>
      <span class="n">itemStringIntMap</span> <span class="k">=</span> <span class="n">itemStringIntMap</span><span class="o">)</span>
  <span class="o">}</span>
</pre></td></tr></tbody></table> </div> <h4 id='working-with-spark-mllib&#39;s-als.train(....)' class='header-anchors'>Working with Spark MLlib&#39;s ALS.train(....)</h4><p>As mentioned above, MLlib&#39;s <code>Rating</code> does not support <code>String</code> user ID and item ID. Its <code>ALS.train</code> thus also assumes <code>Int</code>-only <code>Rating</code>.</p><p>Here you need to map your String-supported <code>Rating</code> to MLlib&#39;s Integer-only <code>Rating</code>. First, you can rename MLlib&#39;s Integer-only <code>Rating</code> to <code>MLlibRating</code> for clarity:</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td class="code"><pre>import org.apache.spark.mllib.recommendation.<span class="o">{</span>Rating <span class="o">=</span>&gt; MLlibRating<span class="o">}</span>
</pre></td></tr></tbody></table> </div> <p>You then create a bi-directional map with <code>BiMap.stringInt</code> which maps each String record to an Integer index.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
2</pre></td><td class="code"><pre>val userStringIntMap <span class="o">=</span> BiMap.stringInt<span class="o">(</span>data.ratings.map<span class="o">(</span>_.user<span class="o">))</span>
val itemStringIntMap <span class="o">=</span> BiMap.stringInt<span class="o">(</span>data.ratings.map<span class="o">(</span>_.item<span class="o">))</span>
</pre></td></tr></tbody></table> </div> <p>Finally, you re-create each <code>Rating</code> event as <code>MLlibRating</code>:</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td class="code"><pre>MLlibRating<span class="o">(</span>userStringIntMap<span class="o">(</span>r.user<span class="o">)</span>, itemStringIntMap<span class="o">(</span>r.item<span class="o">)</span>, r.rating<span class="o">)</span>
</pre></td></tr></tbody></table> </div> <p>In addition to <code>RDD[MLlibRating]</code>, <code>ALS.train</code> takes the following parameters: <em>rank</em>, <em>iterations</em>, <em>lambda</em> and <em>seed</em>.</p><p>The values of these parameters are specified in <em>algorithms</em> of MyRecommendation/<strong><em>engine.json</em></strong>:</p><div class="highlight shell"><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="s2">"algorithms"</span>: <span class="o">[</span>
    <span class="o">{</span>
      <span class="s2">"name"</span>: <span class="s2">"als"</span>,
      <span class="s2">"params"</span>: <span class="o">{</span>
        <span class="s2">"rank"</span>: 10,
        <span class="s2">"numIterations"</span>: 20,
        <span class="s2">"lambda"</span>: 0.01,
        <span class="s2">"seed"</span>: 3
      <span class="o">}</span>
    <span class="o">}</span>
  <span class="o">]</span>
  ...
<span class="o">}</span>
</pre></td></tr></tbody></table> </div> <p>PredictionIO will automatically loads these values into the constructor <code>ap</code>, which has a corresponding case case <code>ALSAlgorithmParams</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="n">rank</span><span class="k">:</span> <span class="kt">Int</span><span class="o">,</span>
  <span class="n">numIterations</span><span class="k">:</span> <span class="kt">Int</span><span class="o">,</span>
  <span class="n">lambda</span><span class="k">:</span> <span class="kt">Double</span><span class="o">,</span>
  <span class="n">seed</span><span class="k">:</span> <span class="kt">Option</span><span class="o">[</span><span class="kt">Long</span><span class="o">])</span> <span class="k">extends</span> <span class="nc">Params</span>
</pre></td></tr></tbody></table> </div> <p>The <code>seed</code> parameter is an optional parameter, which is used by MLlib ALS algorithm internally to generate random values. If the <code>seed</code> is not specified, current system time would be used and hence each train may produce different reuslts. Specify a fixed value for the <code>seed</code> if you want to have deterministic result (For example, when you are testing).</p><p><code>ALS.train</code> then returns a <code>MatrixFactorizationModel</code> model which contains RDD data. RDD is a distributed collection of items which <em>does not</em> persist. To store the model, you convert the model to <code>ALSModel</code> class at the end. <code>ALSModel</code> is a persistable class that extends <code>MatrixFactorizationModel</code>.</p> <blockquote> <p>The detailed implementation can be found at MyRecommendation/src/main/scala/<strong><em>ALSModel.scala</em></strong></p></blockquote> <p>PredictionIO will automatically store the returned model, i.e. <code>ALSModel</code> in this case.</p><h3 id='predict(...)' class='header-anchors'>predict(...)</h3><p><code>predict</code> is called when you send a JSON query to <a href="http://localhost:8000/queries.json">http://localhost:8000/queries.json</a>. PredictionIO converts the query, such as <code>{ &quot;user&quot;: &quot;1&quot;, &quot;num&quot;: 4 }</code> to the <code>Query</code> class you defined previously.</p><p>The predictive model <code>MatrixFactorizationModel</code> of MLlib ALS, which is now extended as <code>ALSModel</code>, offers a method called <code>recommendProducts</code>. <code>recommendProducts</code> takes two parameters: user id (i.e. the <code>Int</code> index of <code>query.user</code>) and the number of items to be returned (i.e. <code>query.num</code>). It predicts the top <em>num</em> of items a user will like.</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">// Convert String ID to Int index for Mllib
</span>    <span class="n">model</span><span class="o">.</span><span class="n">userStringIntMap</span><span class="o">.</span><span class="n">get</span><span class="o">(</span><span class="n">query</span><span class="o">.</span><span class="n">user</span><span class="o">).</span><span class="n">map</span> <span class="o">{</span> <span class="n">userInt</span> <span class="k">=&gt;</span>
      <span class="c1">// create inverse view of itemStringIntMap
</span>      <span class="k">val</span> <span class="n">itemIntStringMap</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">itemStringIntMap</span><span class="o">.</span><span class="n">inverse</span>
      <span class="c1">// recommendProducts() returns Array[MLlibRating], which uses item Int
</span>      <span class="c1">// index. Convert it to String ID for returning PredictedResult
</span>      <span class="k">val</span> <span class="n">itemScores</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">recommendProducts</span><span class="o">(</span><span class="n">userInt</span><span class="o">,</span> <span class="n">query</span><span class="o">.</span><span class="n">num</span><span class="o">)</span>
        <span class="o">.</span><span class="n">map</span> <span class="o">(</span><span class="n">r</span> <span class="k">=&gt;</span> <span class="nc">ItemScore</span><span class="o">(</span><span class="n">itemIntStringMap</span><span class="o">(</span><span class="n">r</span><span class="o">.</span><span class="n">product</span><span class="o">),</span> <span class="n">r</span><span class="o">.</span><span class="n">rating</span><span class="o">))</span>
      <span class="k">new</span> <span class="nc">PredictedResult</span><span class="o">(</span><span class="n">itemScores</span><span class="o">)</span>
    <span class="o">}.</span><span class="n">getOrElse</span><span class="o">{</span>
      <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="o">(</span><span class="n">s</span><span class="s">"No prediction for unknown user ${query.user}."</span><span class="o">)</span>
      <span class="k">new</span> <span class="nc">PredictedResult</span><span class="o">(</span><span class="nc">Array</span><span class="o">.</span><span class="n">empty</span><span class="o">)</span>
    <span class="o">}</span>
  <span class="o">}</span>
</pre></td></tr></tbody></table> </div> <p>Note that <code>recommendProducts</code> returns the <code>Int</code> indices of items. You map them back to <code>String</code> with <code>itemIntStringMap</code> before they are returned.</p> <blockquote> <p>You have defined the class <code>PredictedResult</code> earlier.</p></blockquote> <p>PredictionIO passes the returned <code>PredictedResult</code> object to <em>Serving</em>.</p><h2 id='serving' class='header-anchors'>Serving</h2><p>The <code>serve</code> method of class <code>Serving</code> processes predicted result. It is also responsible for combining multiple predicted results into one if you have more than one predictive model. <em>Serving</em> then returns the final predicted result. PredictionIO will convert it to a JSON response automatically.</p><p>In MyRecommendation/src/main/scala/<strong><em>Serving.scala</em></strong>,</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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9</pre></td><td class="code"><pre><span class="k">class</span> <span class="nc">Serving</span>
  <span class="k">extends</span> <span class="nc">LServing</span><span class="o">[</span><span class="kt">Query</span>, <span class="kt">PredictedResult</span><span class="o">]</span> <span class="o">{</span>

  <span class="k">override</span>
  <span class="k">def</span> <span class="n">serve</span><span class="o">(</span><span class="n">query</span><span class="k">:</span> <span class="kt">Query</span><span class="o">,</span>
    <span class="n">predictedResults</span><span class="k">:</span> <span class="kt">Seq</span><span class="o">[</span><span class="kt">PredictedResult</span><span class="o">])</span><span class="k">:</span> <span class="kt">PredictedResult</span> <span class="o">=</span> <span class="o">{</span>
    <span class="n">predictedResults</span><span class="o">.</span><span class="n">head</span>
  <span class="o">}</span>
<span class="o">}</span>
</pre></td></tr></tbody></table> </div> <p>When you send a JSON query to <a href="http://localhost:8000/queries.json">http://localhost:8000/queries.json</a>, <code>PredictedResult</code> from all models will be passed to <code>serve</code> as a sequence, i.e. <code>Seq[PredictedResult]</code>.</p> <blockquote> <p>An engine can train multiple models if you specify more than one Algorithm component in <code>object RecommendationEngine</code> inside <strong><em>Engine.scala</em></strong>. Since only one <code>ALSAlgorithm</code> is implemented by default, this <code>Seq</code> contains one element.</p></blockquote> <p>Now you should have a good understanding of the DASE model. We will show you an example of customizing the Data Preparator to exclude certain items from your training set.</p><h4 id='<a-href="/templates/recommendation/reading-custom-events/">next:-reading-custom-events</a>' class='header-anchors' ><a href="/templates/recommendation/reading-custom-events/">Next: Reading Custom Events</a></h4></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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