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<!DOCTYPE html><html><head><title>Batch Persistable Evaluator (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="Batch Persistable Evaluator (Recommendation)"/><link rel="canonical" href="https://docs.prediction.io/templates/recommendation/batch-evaluator/"/><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>Batch Persistable Evaluator (Recommendation)</h1></div></div><div id="table-of-content-wrapper"><h5>On this page</h5><aside id="table-of-contents"><ul> <li> <a href="#1-modify-datasource">1. Modify DataSource</a> </li> <li> <a href="#2-add-a-new-evaluator">2. Add a new Evaluator</a> </li> <li> <a href="#3-define-evaluation-and-engineparamsgenerator-object">3. Define Evaluation and EngineParamsGenerator object</a> </li> <li> <a href="#4-build-and-run">4. build and run</a> </li> </ul> </aside><hr/><a id="edit-page-link" href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/templates/recommendation/batch-evaluator.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>Batch Persistable Evaluator (Recommendation)</h1></div></div><div class="content"><p>This how-to tutorial would explain how you can also use <code>$pio eval</code> to persist predicted result for a batch of queries. Please read the <a href="/templates/recommendation/evaluation/">Evaluation</a> to understand the usage of DataSoure&#39;s <code>readEval()</code> and the Evaluation component first.</p><div class="alert-message warning"><p>This tutorial is based on some experimental and developer features, which may be changed in future release.</p></div><div class="alert-message note"><p>This tutorial is based on Recommendation template version v0.3.2</p></div><h2 id='1.-modify-datasource' class='header-anchors'>1. Modify DataSource</h2><p>Modify DataSource&#39;s <code>readEval()</code> to generate the batch Queries which you want to run batch predict.</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">override</span>
<span class="k">def</span> <span class="n">readEval</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">Seq</span><span class="o">[(</span><span class="kt">TrainingData</span>, <span class="kt">EmptyEvaluationInfo</span>, <span class="kt">RDD</span><span class="o">[(</span><span class="kt">Query</span>, <span class="kt">ActualResult</span><span class="o">)])]</span> <span class="k">=</span> <span class="o">{</span>
<span class="c1">// This function only return one evaluation data set
</span>
<span class="c1">// Create your own queries here. Below are provided as examples.
</span> <span class="c1">// for example, you may get all distinct user id from the trainingData to create the Query
</span> <span class="k">val</span> <span class="n">batchQueries</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">Query</span><span class="o">]</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="nc">Query</span><span class="o">(</span><span class="n">user</span> <span class="k">=</span> <span class="s">"1"</span><span class="o">,</span> <span class="n">num</span> <span class="k">=</span> <span class="mi">10</span><span class="o">),</span>
<span class="nc">Query</span><span class="o">(</span><span class="n">user</span> <span class="k">=</span> <span class="s">"3"</span><span class="o">,</span> <span class="n">num</span> <span class="k">=</span> <span class="mi">15</span><span class="o">),</span>
<span class="nc">Query</span><span class="o">(</span><span class="n">user</span> <span class="k">=</span> <span class="s">"5"</span><span class="o">,</span> <span class="n">num</span> <span class="k">=</span> <span class="mi">20</span><span class="o">)</span>
<span class="o">)</span>
<span class="o">)</span>
<span class="k">val</span> <span class="n">queryAndActual</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[(</span><span class="kt">Query</span>, <span class="kt">ActualResult</span><span class="o">)]</span> <span class="k">=</span> <span class="n">batchQueries</span><span class="o">.</span><span class="n">map</span> <span class="o">(</span><span class="n">q</span> <span class="k">=&gt;</span>
<span class="c1">// the ActualResult contain dummy empty rating array
</span> <span class="c1">// because we not interested in Actual result for batch predict purpose.
</span> <span class="o">(</span><span class="n">q</span><span class="o">,</span> <span class="nc">ActualResult</span><span class="o">(</span><span class="nc">Array</span><span class="o">()))</span>
<span class="o">)</span>
<span class="k">val</span> <span class="n">evalDataSet</span> <span class="k">=</span> <span class="o">(</span>
<span class="n">readTraining</span><span class="o">(</span><span class="n">sc</span><span class="o">),</span>
<span class="k">new</span> <span class="nc">EmptyEvaluationInfo</span><span class="o">(),</span>
<span class="n">queryAndActual</span>
<span class="o">)</span>
<span class="nc">Seq</span><span class="o">(</span><span class="n">evalDataSet</span><span class="o">)</span>
<span class="o">}</span>
</pre></td></tr></tbody></table> </div> <div class="alert-message note"><p>Alternatively, you can create a new DataSource extending original DataSource. Then you can add the new one in Engine.scala and specify which one to use in engine.json.</p></div> <h2 id='2.-add-a-new-evaluator' class='header-anchors'>2. Add a new Evaluator</h2><p>Create a new file <code>BatchPersistableEvaluator.scala</code>. Unlike the <code>MetricEvaluator</code>, this Evaluator simply writes the Query and correpsonding PredictedResult to the output directory without performaning any metrics calculation.</p><p>Note that output directory is specified by the variable <code>outputDir</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">import</span> <span class="nn">io.prediction.controller.EmptyEvaluationInfo</span>
<span class="k">import</span> <span class="nn">io.prediction.controller.Engine</span>
<span class="k">import</span> <span class="nn">io.prediction.controller.EngineParams</span>
<span class="k">import</span> <span class="nn">io.prediction.controller.EngineParamsGenerator</span>
<span class="k">import</span> <span class="nn">io.prediction.controller.Evaluation</span>
<span class="k">import</span> <span class="nn">io.prediction.controller.Params</span>
<span class="k">import</span> <span class="nn">io.prediction.core.BaseEvaluator</span>
<span class="k">import</span> <span class="nn">io.prediction.core.BaseEvaluatorResult</span>
<span class="k">import</span> <span class="nn">io.prediction.workflow.WorkflowParams</span>
<span class="k">import</span> <span class="nn">org.apache.spark.SparkContext</span>
<span class="k">import</span> <span class="nn">org.apache.spark.rdd.RDD</span>
<span class="k">import</span> <span class="nn">org.json4s.DefaultFormats</span>
<span class="k">import</span> <span class="nn">org.json4s.Formats</span>
<span class="k">import</span> <span class="nn">org.json4s.native.Serialization</span>
<span class="k">import</span> <span class="nn">grizzled.slf4j.Logger</span>
<span class="k">class</span> <span class="nc">BatchPersistableEvaluatorResult</span> <span class="k">extends</span> <span class="nc">BaseEvaluatorResult</span> <span class="o">{}</span>
<span class="k">class</span> <span class="nc">BatchPersistableEvaluator</span> <span class="k">extends</span> <span class="nc">BaseEvaluator</span><span class="o">[</span>
<span class="kt">EmptyEvaluationInfo</span>,
<span class="kt">Query</span>,
<span class="kt">PredictedResult</span>,
<span class="kt">ActualResult</span>,
<span class="kt">BatchPersistableEvaluatorResult</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="c1">// A helper object for the json4s serialization
</span> <span class="k">case</span> <span class="k">class</span> <span class="nc">Row</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">predictedResult</span><span class="k">:</span> <span class="kt">PredictedResult</span><span class="o">)</span>
<span class="k">extends</span> <span class="nc">Serializable</span>
<span class="k">def</span> <span class="n">evaluateBase</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">evaluation</span><span class="k">:</span> <span class="kt">Evaluation</span><span class="o">,</span>
<span class="n">engineEvalDataSet</span><span class="k">:</span> <span class="kt">Seq</span><span class="o">[(</span>
<span class="kt">EngineParams</span>,
<span class="kt">Seq</span><span class="o">[(</span><span class="kt">EmptyEvaluationInfo</span>, <span class="kt">RDD</span><span class="o">[(</span><span class="kt">Query</span>, <span class="kt">PredictedResult</span>, <span class="kt">ActualResult</span><span class="o">)])])],</span>
<span class="n">params</span><span class="k">:</span> <span class="kt">WorkflowParams</span><span class="o">)</span><span class="k">:</span> <span class="kt">BatchPersistableEvaluatorResult</span> <span class="o">=</span> <span class="o">{</span>
<span class="cm">/** Extract the first data, as we are only interested in the first
* evaluation. It is possible to relax this restriction, and have the
* output logic below to write to different directory for different engine
* params.
*/</span>
<span class="n">require</span><span class="o">(</span>
<span class="n">engineEvalDataSet</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="mi">1</span><span class="o">,</span> <span class="s">"There should be only one engine params"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">evalDataSet</span> <span class="k">=</span> <span class="n">engineEvalDataSet</span><span class="o">.</span><span class="n">head</span><span class="o">.</span><span class="n">_2</span>
<span class="n">require</span><span class="o">(</span><span class="n">evalDataSet</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="mi">1</span><span class="o">,</span> <span class="s">"There should be only one RDD[(Q, P, A)]"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">qpaRDD</span> <span class="k">=</span> <span class="n">evalDataSet</span><span class="o">.</span><span class="n">head</span><span class="o">.</span><span class="n">_2</span>
<span class="c1">// qpaRDD contains 3 queries we specified in readEval, the corresponding
</span> <span class="c1">// predictedResults, and the dummy actual result.
</span>
<span class="cm">/** The output directory. Better to use absolute path if you run on cluster.
* If your database has a Hadoop interface, you can also convert the
* following to write to your database in parallel as well.
*/</span>
<span class="k">val</span> <span class="n">outputDir</span> <span class="k">=</span> <span class="s">"batch_result"</span>
<span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="o">(</span><span class="s">"Writing result to disk"</span><span class="o">)</span>
<span class="n">qpaRDD</span>
<span class="o">.</span><span class="n">map</span> <span class="o">{</span> <span class="k">case</span> <span class="o">(</span><span class="n">q</span><span class="o">,</span> <span class="n">p</span><span class="o">,</span> <span class="n">a</span><span class="o">)</span> <span class="k">=&gt;</span> <span class="nc">Row</span><span class="o">(</span><span class="n">q</span><span class="o">,</span> <span class="n">p</span><span class="o">)</span> <span class="o">}</span>
<span class="o">.</span><span class="n">map</span> <span class="o">{</span> <span class="n">row</span> <span class="k">=&gt;</span>
<span class="c1">// Convert into a json
</span> <span class="k">implicit</span> <span class="k">val</span> <span class="n">formats</span><span class="k">:</span> <span class="kt">Formats</span> <span class="o">=</span> <span class="nc">DefaultFormats</span>
<span class="nc">Serialization</span><span class="o">.</span><span class="n">write</span><span class="o">(</span><span class="n">row</span><span class="o">)</span>
<span class="o">}</span>
<span class="o">.</span><span class="n">saveAsTextFile</span><span class="o">(</span><span class="n">outputDir</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">"Result can be found in $outputDir"</span><span class="o">)</span>
<span class="k">new</span> <span class="nc">BatchPersistableEvaluatorResult</span><span class="o">()</span>
<span class="o">}</span>
<span class="o">}</span>
</pre></td></tr></tbody></table> </div> <h2 id='3.-define-evaluation-and-engineparamsgenerator-object' class='header-anchors'>3. Define Evaluation and EngineParamsGenerator object</h2><p>Create a new file <code>BatchEvaluation.scala</code>. Note that the new <code>BatchPersistableEvaluator</code> is used. The <code>BatchEngineParamsList</code> specifies the parameters of the engine.</p><p>Modify the appName parameter in <code>DataSourceParams</code> to match your app name.</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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14
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29</pre></td><td class="code"><pre><span class="k">package</span> <span class="nn">org.template.recommendation</span>
<span class="k">import</span> <span class="nn">io.prediction.controller.EngineParamsGenerator</span>
<span class="k">import</span> <span class="nn">io.prediction.controller.EngineParams</span>
<span class="k">import</span> <span class="nn">io.prediction.controller.Evaluation</span>
<span class="k">object</span> <span class="nc">BatchEvaluation</span> <span class="k">extends</span> <span class="nc">Evaluation</span> <span class="o">{</span>
<span class="c1">// Define Engine and Evaluator used in Evaluation
</span>
<span class="cm">/**
* Specify the new BatchPersistableEvaluator.
*/</span>
<span class="n">engineEvaluator</span> <span class="k">=</span>
<span class="o">(</span><span class="nc">RecommendationEngine</span><span class="o">(),</span> <span class="k">new</span> <span class="nc">BatchPersistableEvaluator</span><span class="o">())</span>
<span class="o">}</span>
<span class="k">object</span> <span class="nc">BatchEngineParamsList</span> <span class="k">extends</span> <span class="nc">EngineParamsGenerator</span> <span class="o">{</span>
<span class="c1">// We only interest in a single engine params.
</span> <span class="n">engineParamsList</span> <span class="k">=</span> <span class="nc">Seq</span><span class="o">(</span>
<span class="nc">EngineParams</span><span class="o">(</span>
<span class="n">dataSourceParams</span> <span class="k">=</span>
<span class="nc">DataSourceParams</span><span class="o">(</span><span class="n">appName</span> <span class="k">=</span> <span class="s">"INVALID_APP_NAME"</span><span class="o">,</span> <span class="n">evalParams</span> <span class="k">=</span> <span class="nc">None</span><span class="o">),</span>
<span class="n">algorithmParamsList</span> <span class="k">=</span> <span class="nc">Seq</span><span class="o">((</span><span class="s">"als"</span><span class="o">,</span> <span class="nc">ALSAlgorithmParams</span><span class="o">(</span>
<span class="n">rank</span> <span class="k">=</span> <span class="mi">10</span><span class="o">,</span>
<span class="n">numIterations</span> <span class="k">=</span> <span class="mi">20</span><span class="o">,</span>
<span class="n">lambda</span> <span class="k">=</span> <span class="mf">0.01</span><span class="o">,</span>
<span class="n">seed</span> <span class="k">=</span> <span class="nc">Some</span><span class="o">(</span><span class="mi">3L</span><span class="o">))))))</span>
<span class="o">}</span>
</pre></td></tr></tbody></table> </div> <h2 id='4.-build-and-run' class='header-anchors'>4. build and run</h2><p>Run the following command to build</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>pio build
</pre></td></tr></tbody></table> </div> <p>After the build is successful, you should see the following outputs:</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="o">[</span>INFO] <span class="o">[</span>Console<span class="nv">$]</span> Your engine is ready <span class="k">for </span>training.
</pre></td></tr></tbody></table> </div> <p>To run the <code>BatchEvaluation</code> with <code>BatchEngineParamsList</code>, run the following command:</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td class="code"><pre><span class="gp">$ </span>pio <span class="nb">eval </span>org.template.recommendation.BatchEvaluation org.template.recommendation.BatchEngineParamsList
</pre></td></tr></tbody></table> </div> <p>You should see the following outputs:</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
4</pre></td><td class="code"><pre><span class="o">[</span>INFO] <span class="o">[</span>BatchPersistableEvaluator] Writing result to disk
<span class="o">[</span>INFO] <span class="o">[</span>BatchPersistableEvaluator] Result can be found <span class="k">in </span>batch_result
<span class="o">[</span>INFO] <span class="o">[</span>CoreWorkflow<span class="nv">$]</span> Updating evaluation instance with result: org.template.recommendation.BatchPersistableEvaluatorResult@2f886889
<span class="o">[</span>INFO] <span class="o">[</span>CoreWorkflow<span class="nv">$]</span> runEvaluation completed
</pre></td></tr></tbody></table> </div> <p>You should find the batch queries and the predicted results in the output directory <code>batch_result/</code>.</p></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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