| <!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'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 "MyRecommendation" 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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| 7</pre></td><td class="code"><pre><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="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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| 16</pre></td><td class="code"><pre><span class="k">import</span> <span class="nn">scala.io.Source</span> <span class="c1">// ADDED |
| </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">=></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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| 7</pre></td><td class="code"><pre>... |
| <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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| 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>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>{ "user": "1", "num": 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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| 19</pre></td><td class="code"><pre><span class="k">import</span> <span class="nn">io.prediction.controller.Params</span> <span class="c1">// ADDED |
| </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">=></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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