<!DOCTYPE html><html><head><title>Customizing Data Preparator (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="Customizing Data Preparator (Recommendation)"/><link rel="canonical" href="https://docs.prediction.io/templates/recommendation/customize-data-prep/"/><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>Customizing Data Preparator (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-data-preparator-component">The Data Preparator Component</a> </li> <li> <a href="#modify-the-preparator">Modify the Preparator</a> </li> <li> <a href="#deploy-the-modified-engine">Deploy the Modified Engine</a> </li> <li> <a href="#adding-preparator-parameters">Adding Preparator Parameters</a> </li> </ul> </aside><hr/><a id="edit-page-link" href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/templates/recommendation/customize-data-prep.html.md"><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>Customizing Data Preparator (Recommendation)</h1></div></div><div class="content"><p>Data Preparator is where pre-processing actions occurs. For example, one may want to remove some very popular items from the training data because she thinks that these items may not help finding individual person&#39;s tastes or one may have a black list of item that she wants to remove from the training data before feeding it to the algorithm.</p><p>This section assumes that you have created a <em>MyRecommendation</em> engine based on the <a href="/templates/recommendation/quickstart/">Recommendation Engine Template: QuickStart</a>. We will demonstrate how to add a filtering logic to exclude a list of items in the training data.</p><p>A sample black list file containing the items to be excluded is provided in <code>./data/sample_not_train_data.txt</code>.</p><p>A full end-to-end example can be found on <a href="https://github.com/PredictionIO/PredictionIO/tree/develop/examples/scala-parallel-recommendation/custom-prepartor">GitHub</a>.</p><h2 id='the-data-preparator-component' class='header-anchors'>The Data Preparator Component</h2><p>Recall <a href="/start/engines/">the DASE Architecture</a>, data is prepared by 2 components sequentially: <em>Data Source</em> and <em>Data Preparator</em>. <em>Data Source</em> reads data from the data store of Event Server and then <em>Data Preparator</em> prepares <code>RDD[Rating]</code> for the ALS algorithm.</p><p>You may modify any component in an engine template to fit your needs. This example shows you how to add the filtering logics in Data Preparator.</p><h2 id='modify-the-preparator' class='header-anchors'>Modify the Preparator</h2><p>The Data Preparator component can be found in <code>src/main/scala/Preparator.scala</code> in the &quot;MyRecommendation&quot; directory. The unmodified version looks like the 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">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>
</pre></td></tr></tbody></table> </div> <p>The <code>prepare</code> method simply passes the ratings from <code>TrainingData</code> to <code>PreparedData</code>.</p><p>You can modify the <code>prepare</code> method to read a black list of items from a file and remove them from <code>TrainingData</code>, so it becomes:</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>
<span class="k">class</span> <span class="nc">Preparator</span>
  <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="c1">// MODIFIED HERE
</span>    <span class="k">val</span> <span class="n">noTrainItems</span> <span class="k">=</span> <span class="nc">Source</span><span class="o">.</span><span class="n">fromFile</span><span class="o">(</span><span class="s">"./data/sample_not_train_data.txt"</span><span class="o">)</span>
      <span class="o">.</span><span class="n">getLines</span><span class="o">.</span><span class="n">toSet</span>
    <span class="c1">// exclude noTrainItems from original trainingData
</span>    <span class="k">val</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="n">filter</span><span class="o">(</span> <span class="n">r</span> <span class="k">=&gt;</span>
      <span class="o">!</span><span class="n">noTrainItems</span><span class="o">.</span><span class="n">contains</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="o">)</span>
    <span class="k">new</span> <span class="nc">PreparedData</span><span class="o">(</span><span class="n">ratings</span><span class="o">)</span>
  <span class="o">}</span>
<span class="o">}</span>
</pre></td></tr></tbody></table> </div> <blockquote> <p>We will show you how not to hardcode the path <code>./data/sample_not_train_data.txt</code> soon.</p></blockquote> <h2 id='deploy-the-modified-engine' class='header-anchors'>Deploy the Modified Engine</h2><p>Now you can deploy the modified engine as described in <a href="/templates/recommendation/quickstart/">Quick Start</a>.</p><p>Make sure the <code>appName</code> defined in the file <code>engine.json</code> matches your <em>App Name</em>:</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">"YourAppName"</span>
  <span class="o">}</span>
<span class="o">}</span>,
...
</pre></td></tr></tbody></table> </div> <p>To build <em>MyRecommendation</em> and deploy it as a service:</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="gp">$ </span>pio train
<span class="gp">$ </span>pio deploy
</pre></td></tr></tbody></table> </div> <p>This will deploy an engine that binds to <a href="http://localhost:8000">http://localhost:8000</a>. You can visit that page in your web browser to check its status.</p><p>Now, You can try to retrieve predicted results. To recommend 4 movies to user whose ID is 1, send this JSON <code>{ &quot;user&quot;: &quot;1&quot;, &quot;num&quot;: 4 }</code> to the deployed engine</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><span class="gp">$ </span>curl -H <span class="s2">"Content-Type: application/json"</span> -d <span class="s1">'{ "user": "1", "num": 4 }'</span> http://localhost:8000/queries.json
</pre></td></tr></tbody></table> </div> <p>and it will return a JSON of recommended movies.</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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8</pre></td><td class="code"><pre><span class="p">{</span><span class="w">
  </span><span class="s2">"itemScores"</span><span class="p">:</span><span class="w"> </span><span class="p">[</span><span class="w">
    </span><span class="p">{</span><span class="s2">"item"</span><span class="p">:</span><span class="w"> </span><span class="s2">"22"</span><span class="p">,</span><span class="w"> </span><span class="s2">"score"</span><span class="p">:</span><span class="w"> </span><span class="mf">4.072304374729956</span><span class="p">},</span><span class="w">
    </span><span class="p">{</span><span class="s2">"item"</span><span class="p">:</span><span class="w"> </span><span class="s2">"62"</span><span class="p">,</span><span class="w"> </span><span class="s2">"score"</span><span class="p">:</span><span class="w"> </span><span class="mf">4.058482414005789</span><span class="p">},</span><span class="w">
    </span><span class="p">{</span><span class="s2">"item"</span><span class="p">:</span><span class="w"> </span><span class="s2">"75"</span><span class="p">,</span><span class="w"> </span><span class="s2">"score"</span><span class="p">:</span><span class="w"> </span><span class="mf">4.046063009943821</span><span class="p">},</span><span class="w">
    </span><span class="p">{</span><span class="s2">"item"</span><span class="p">:</span><span class="w"> </span><span class="s2">"68"</span><span class="p">,</span><span class="w"> </span><span class="s2">"score"</span><span class="p">:</span><span class="w"> </span><span class="mf">3.8153661512945325</span><span class="p">}</span><span class="w">
  </span><span class="p">]</span><span class="w">
</span><span class="p">}</span><span class="w">
</span></pre></td></tr></tbody></table> </div> <p>Congratulations! You have learned how to add customized logic to your Data Preparator!</p><h2 id='adding-preparator-parameters' class='header-anchors'>Adding Preparator Parameters</h2><p>Optionally, you may want to take the hardcoded path (<code>./data/sample_not_train_data.txt</code>) away from the source code.</p><p>PredictionIO offers <code>PreparatorParams</code> so you can read variable values from <code>engine.json</code> instead.</p><p>Modify <code>src/main/scala/Preparator.scala</code> again in the <em>MyRecommendation</em> directory to:</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>
 <span class="c1">// ADDED CustomPreparatorParams case class
</span><span class="k">case</span> <span class="k">class</span> <span class="nc">CustomPreparatorParams</span><span class="o">(</span>
  <span class="n">filepath</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">Preparator</span><span class="o">(</span><span class="n">pp</span><span class="k">:</span> <span class="kt">CustomPreparatorParams</span><span class="o">)</span> <span class="c1">// ADDED CustomPreparatorParams
</span>  <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">val</span> <span class="n">noTrainItems</span> <span class="k">=</span> <span class="nc">Source</span><span class="o">.</span><span class="n">fromFile</span><span class="o">(</span><span class="n">pp</span><span class="o">.</span><span class="n">filepath</span><span class="o">).</span><span class="n">getLines</span><span class="o">.</span><span class="n">toSet</span> <span class="c1">//CHANGED
</span>    <span class="k">val</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="n">filter</span><span class="o">(</span> <span class="n">r</span> <span class="k">=&gt;</span>
      <span class="o">!</span><span class="n">noTrainItems</span><span class="o">.</span><span class="n">contains</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="o">)</span>
    <span class="k">new</span> <span class="nc">PreparedData</span><span class="o">(</span><span class="n">ratings</span><span class="o">)</span>
  <span class="o">}</span>
<span class="o">}</span>

</pre></td></tr></tbody></table> </div> <p>In <code>engine.json</code>, you define the parameters <code>filepath</code> for the Data Preparator:</p><div class="highlight json"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
2
3
4
5
6
7
8
9</pre></td><td class="code"><pre><span class="p">{</span><span class="w">
  </span><span class="err">...</span><span class="w">
  </span><span class="s2">"preparator"</span><span class="p">:</span><span class="w"> </span><span class="p">{</span><span class="w">
    </span><span class="s2">"params"</span><span class="p">:</span><span class="w"> </span><span class="p">{</span><span class="w">
      </span><span class="s2">"filepath"</span><span class="p">:</span><span class="w"> </span><span class="s2">"./data/sample_not_train_data.txt"</span><span class="w">
    </span><span class="p">}</span><span class="w">
  </span><span class="p">},</span><span class="w">
  </span><span class="err">...</span><span class="w">
</span><span class="err">}</span><span class="w">
</span></pre></td></tr></tbody></table> </div> <p>Try to build <em>MyRecommendation</em> and deploy it again:</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
3</pre></td><td class="code"><pre><span class="gp">$ </span>pio build
<span class="gp">$ </span>pio train
<span class="gp">$ </span>pio deploy
</pre></td></tr></tbody></table> </div> <p>You can change the <code>filepath</code> value without re-building the code next time.</p><h4 id='<a-href="/templates/recommendation/customize-serving/">next:-customizing-serving</a>' class='header-anchors' ><a href="/templates/recommendation/customize-serving/">Next: Customizing Serving</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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