http://git-wip-us.apache.org/repos/asf/incubator-predictionio-site/blob/2e6db646/templates/classification/add-algorithm/index.html ---------------------------------------------------------------------- diff --git a/templates/classification/add-algorithm/index.html b/templates/classification/add-algorithm/index.html deleted file mode 100644 index fd24884..0000000 --- a/templates/classification/add-algorithm/index.html +++ /dev/null @@ -1,147 +0,0 @@ -<!DOCTYPE html><html><head><title>Using Alternative Algorithm</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="Using Alternative Algorithm"/><link rel="canonical" href="https://predictionio.incubator.apache.org/templates/classification/add-algorithm/"/><link href="/images/favicon/normal-b330020a.png" rel="shortcut icon"/><link 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href="#create-a-new-file-randomforestalgorithm-scala">Create a new file RandomForestAlgorithm.scala</a> </li> <li> <a href="#define-the-algorithm-class-and-parameters">Define the algorithm class and parameters</a> </li> <li> <a href="#update-engine-scala">Update Engine.scala</a> </li> <li> <a href="#update-engine-json">Update engine.json</a> </li> </ul> </aside><hr/><a id="edit-page-link" href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/templates/classification/add-algorithm.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="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a href="#">PredictionIO Official Templates</a><span class= "spacer">></span></li><li><a href="#">Classification</a><span class="spacer">></span></li><li><span class="last">Use Alternative Algorithm</span></li></ul></div><div id="page-title"><h1>Using Alternative Algorithm</h1></div></div><div class="content"> <p>The classification template uses the Naive Bayes algorithm by default. You can easily add and use other MLlib classification algorithms. The following will demonstrate how to add the <a href="https://spark.apache.org/docs/latest/mllib-ensembles.html";>MLlib Random Forests algorithm</a> into the engine.</p><p>You can find the complete modified source code <a href="https://github.com/apache/incubator-predictionio/tree/develop/examples/scala-parallel-classification/add-algorithm";>here</a>.</p><h2 id='create-a-new-file-randomforestalgorithm.scala' class='header-anchors'>Create a new file RandomForestAlgorithm.scala</h2><p>Locate <code>src/main/scala/NaiveBayesAlgorithm.scala</code> under your engine directory, which should be /MyCl assification if you are following the <a href="/templates/classification/quickstart/">Classification QuickStart</a>. Copy <code>NaiveBayesAlgorithm.scala</code> and create a new file <code>RandomForestAlgorithm.scala</code>. You will modify this file and follow the instructions below to define a new RandomForestAlgorithm class.</p><h2 id='define-the-algorithm-class-and-parameters' class='header-anchors'>Define the algorithm class and parameters</h2><p>In 'RandomForestAlgorithm.scala', import the MLlib Random Forests algorithm by changing the following lines:</p><p>Original</p><div class="highlight scala"><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><span class="k">import</span> <span class="nn">org.apache.spark.mllib.classification.NaiveBayes</span> -<span class="k">import</span> <span class="nn">org.apache.spark.mllib.classification.NaiveBayesModel</span> -</pre></td></tr></tbody></table> </div> <p>Change 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 -2</pre></td><td class="code"><pre><span class="k">import</span> <span class="nn">org.apache.spark.mllib.tree.RandomForest</span> <span class="c1">// CHANGED -</span><span class="k">import</span> <span class="nn">org.apache.spark.mllib.tree.model.RandomForestModel</span> <span class="c1">// CHANGED -</span></pre></td></tr></tbody></table> </div> <p>These are the necessary classes in order to use the MLLib's Random Forest algorithm.</p><p>Modify the <code>AlgorithmParams</code> class for the Random Forest algorithm:</p><div class="highlight scala"><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="c1">// CHANGED -</span><span class="k">case</span> <span class="k">class</span> <span class="nc">RandomForestAlgorithmParams</span><span class="o">(</span> - <span class="n">numClasses</span><span class="k">:</span> <span class="kt">Int</span><span class="o">,</span> - <span class="n">numTrees</span><span class="k">:</span> <span class="kt">Int</span><span class="o">,</span> - <span class="n">featureSubsetStrategy</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span> - <span class="n">impurity</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span> - <span class="n">maxDepth</span><span class="k">:</span> <span class="kt">Int</span><span class="o">,</span> - <span class="n">maxBins</span><span class="k">:</span> <span class="kt">Int</span> -<span class="o">)</span> <span class="k">extends</span> <span class="nc">Params</span> -</pre></td></tr></tbody></table> </div> <p>This class defines the parameters of the Random Forest algorithm (which later you can specify the value in engine.json). Please refer to <a href="https://spark.apache.org/docs/latest/mllib-ensembles.html";>MLlib documentation</a> for the description and usage of these parameters.</p><p>Modify the <code>NaiveBayesAlgorithm</code> class to <code>RandomForestAlgorithm</code>. The changes are:</p> <ul> <li>The new <code>RandomForestAlgorithmParams</code> class is used as parameter.</li> <li><code>RandomForestModel</code> is used in type parameter. This is the model returned by the Random Forest algorithm.</li> <li>the <code>train()</code> function is modified and it returns the <code>RandomForestModel</code> instead of <code>NaiveBayesModel</code>.</li> <li>the <code>predict()</code> function takes the <code>RandomForestModel</code> as input.</li> </ul> <div class="highlight scala"><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 -10 -11 -12 -13 -14 -15 -16 -17 -18 -19 -20 -21 -22 -23 -24 -25 -26 -27 -28 -29 -30 -31 -32 -33</pre></td><td class="code"><pre><span class="c1">// extends P2LAlgorithm because the MLlib's RandomForestModel doesn't -// contain RDD. -</span><span class="k">class</span> <span class="nc">RandomForestAlgorithm</span><span class="o">(</span><span class="k">val</span> <span class="n">ap</span><span class="k">:</span> <span class="kt">RandomForestAlgorithmParams</span><span class="o">)</span> <span class="c1">// CHANGED -</span> <span class="k">extends</span> <span class="n">P2LAlgorithm</span><span class="o">[</span><span class="kt">PreparedData</span>, <span class="kt">RandomForestModel</span>, <span class="kt">//</span> <span class="kt">CHANGED</span> - <span class="kt">Query</span>, <span class="kt">PredictedResult</span><span class="o">]</span> <span class="o">{</span> - - <span class="c1">// CHANGED -</span> <span class="k">def</span> <span class="n">train</span><span class="o">(</span><span class="n">sc</span><span class="k">:</span> <span class="kt">SparkContext</span><span class="o">,</span> <span class="n">data</span><span class="k">:</span> <span class="kt">PreparedData</span><span class="o">)</span><span class="k">:</span> <span class="kt">RandomForestModel</span> <span class="o">=</span> <span class="o">{</span> - <span class="c1">// CHANGED -</span> <span class="c1">// Empty categoricalFeaturesInfo indicates all features are continuous. -</span> <span class="k">val</span> <span class="n">categoricalFeaturesInfo</span> <span class="k">=</span> <span class="nc">Map</span><span class="o">[</span><span class="kt">Int</span>, <span class="kt">Int</span><span class="o">]()</span> - <span class="nc">RandomForest</span><span class="o">.</span><span class="n">trainClassifier</span><span class="o">(</span> - <span class="n">data</span><span class="o">.</span><span class="n">labeledPoints</span><span class="o">,</span> - <span class="n">ap</span><span class="o">.</span><span class="n">numClasses</span><span class="o">,</span> - <span class="n">categoricalFeaturesInfo</span><span class="o">,</span> - <span class="n">ap</span><span class="o">.</span><span class="n">numTrees</span><span class="o">,</span> - <span class="n">ap</span><span class="o">.</span><span class="n">featureSubsetStrategy</span><span class="o">,</span> - <span class="n">ap</span><span class="o">.</span><span class="n">impurity</span><span class="o">,</span> - <span class="n">ap</span><span class="o">.</span><span class="n">maxDepth</span><span class="o">,</span> - <span class="n">ap</span><span class="o">.</span><span class="n">maxBins</span><span class="o">)</span> - <span class="o">}</span> - - <span class="k">def</span> <span class="n">predict</span><span class="o">(</span> - <span class="n">model</span><span class="k">:</span> <span class="kt">RandomForestModel</span><span class="o">,</span> <span class="c1">// CHANGED -</span> <span class="n">query</span><span class="k">:</span> <span class="kt">Query</span><span class="o">)</span><span class="k">:</span> <span class="kt">PredictedResult</span> <span class="o">=</span> <span class="o">{</span> - - <span class="k">val</span> <span class="n">label</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="o">(</span><span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span> - <span class="nc">Array</span><span class="o">(</span><span class="n">query</span><span class="o">.</span><span class="n">attr0</span><span class="o">,</span> <span class="n">query</span><span class="o">.</span><span class="n">attr1</span><span class="o">,</span> <span class="n">query</span><span class="o">.</span><span class="n">attr2</span><span class="o">)</span> - <span class="o">))</span> - <span class="nc">PredictedResult</span><span class="o">(</span><span class="n">label</span><span class="o">)</span> - <span class="o">}</span> - -<span class="o">}</span> -</pre></td></tr></tbody></table> </div> <p>Note that the MLlib Random Forest algorithm takes the same training data as the Naive Bayes algorithm (ie, RDD[LabeledPoint]) so you don't need to modify the <code>DataSource</code> and <code>PreparedData</code> classes. If the new algorithm to be added requires different types of training data, then you need to modify these classes accordingly to accommodate your new algorithm.</p><h2 id='update-engine.scala' class='header-anchors'>Update Engine.scala</h2><p>Modify the EngineFactory to add the new algorithm class <code>RandomForestAlgorithm</code> you just defined and give it a name <code>"randomforest"</code>. The name will be used in <code>engine.json</code> to specify which algorithm to use.</p><div class="highlight scala"><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 -10</pre></td><td class="code"><pre><span class="k">object</span> <span class="nc">ClassificationEngine</span> <span class="k">extends</span> <span class="nc">IEngineFactory</span> <span class="o">{</span> - <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">"naive"</span> <span class="o">-></span> <span class="n">classOf</span><span class="o">[</span><span class="kt">NaiveBayesAlgorithm</span><span class="o">],</span> - <span class="s">"randomforest"</span> <span class="o">-></span> <span class="n">classOf</span><span class="o">[</span><span class="kt">RandomForestAlgorithm</span><span class="o">]),</span> <span class="c1">// ADDED -</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> -</pre></td></tr></tbody></table> </div> <p>This engine factory now returns an engine with two algorithms and they are named as <code>"naive"</code> and <code>"randomforest"</code> respectively.</p><h2 id='update-engine.json' class='header-anchors'>Update engine.json</h2><p>In order to use the new algorithm, you need to modify <code>engine.json</code> to specify the name of the algorithm and the parameters.</p><p>Update the engine.json to use <strong>randomforest</strong>:</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 -10 -11 -12 -13 -14 -15</pre></td><td class="code"><pre><span class="err">...</span><span class="w"> -</span><span class="s2">"algorithms"</span><span class="err">:</span><span class="w"> </span><span class="p">[</span><span class="w"> - </span><span class="p">{</span><span class="w"> - </span><span class="s2">"name"</span><span class="p">:</span><span class="w"> </span><span class="s2">"randomforest"</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">"numClasses"</span><span class="p">:</span><span class="w"> </span><span class="mi">4</span><span class="p">,</span><span class="w"> - </span><span class="s2">"numTrees"</span><span class="p">:</span><span class="w"> </span><span class="mi">5</span><span class="p">,</span><span class="w"> - </span><span class="s2">"featureSubsetStrategy"</span><span class="p">:</span><span class="w"> </span><span class="s2">"auto"</span><span class="p">,</span><span class="w"> - </span><span class="s2">"impurity"</span><span class="p">:</span><span class="w"> </span><span class="s2">"gini"</span><span class="p">,</span><span class="w"> - </span><span class="s2">"maxDepth"</span><span class="p">:</span><span class="w"> </span><span class="mi">4</span><span class="p">,</span><span class="w"> - </span><span class="s2">"maxBins"</span><span class="p">:</span><span class="w"> </span><span class="mi">100</span><span class="w"> - </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><span class="err">...</span><span class="w"> -</span></pre></td></tr></tbody></table> </div> <p>The engine now uses <strong>MLlib Random Forests algorithm</strong> instead of the default Naive Bayes algorithm. You are ready to build, train and deploy the engine as described in <a href="/templates/classification/quickstart/">quickstart</a>.</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> <div class="alert-message info"><p>To switch back using Naive Bayes algorithm, simply modify engine.json.</p></div></div></div></div></div><footer><div class="container"><div class="seperator"></div><div class="row"><div class="col-md-6 footer-link-column"><div class="footer-link-column-row"><h4>Community</h4><ul><li><a href="//predictionio.incubator.apache.org/install/" target="blank">Download</a></li><li><a href="//predictionio.incubator.apache.org/" target="blank">Docs</a></li><li><a href="//github.com/apache/incubator-predictionio" target="blank">GitHub</a></li><li><a href="mailto:[email protected]"; 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 footer-link-column"><div class="footer-link-column-row"><h4>Contribute</h4><ul><li><a href="//predictionio.incubator.apach e.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:[email protected]"; target="blank">Subscribe to Development Mailing List</a></li></ul></div></div></div><div class="row"><div class="col-md-12 footer-link-column"><a class="pull-right" href="http://incubator.apache.org/projects/predictionio.html";><img alt="Apache Incubator" src="/images/logos/apache_incubator-6954bd16.png"/></a><span>Apache PredictionIO is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. 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http://git-wip-us.apache.org/repos/asf/incubator-predictionio-site/blob/2e6db646/templates/classification/add-algorithm/index.html.gz ---------------------------------------------------------------------- diff --git a/templates/classification/add-algorithm/index.html.gz b/templates/classification/add-algorithm/index.html.gz deleted file mode 100644 index 26ed250..0000000 Binary files a/templates/classification/add-algorithm/index.html.gz and /dev/null differ http://git-wip-us.apache.org/repos/asf/incubator-predictionio-site/blob/2e6db646/templates/classification/dase/index.html ---------------------------------------------------------------------- diff --git a/templates/classification/dase/index.html b/templates/classification/dase/index.html deleted file mode 100644 index 87223d4..0000000 --- a/templates/classification/dase/index.html +++ /dev/null @@ -1,218 +0,0 @@ -<!DOCTYPE html><html><head><title>DASE Components Explained (Classification)</title><meta charset="utf-8"/><meta 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class="container-fluid"><div class="row"><div id="left-menu-wrapper" class="col-md-3"><nav id="nav-main"><ul><li class="level-1"><a class="expandible" href="/"><span>Apache PredictionIO (incubating) Documentation</span></a><ul><li class="level-2"><a class="final" href="/"><span>Welcome to Apache PredictionIO (incubating)</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Getting Started</span></a><ul><li class="level-2"><a class="final" href="/start/"><span>A Quick Intro</span></a></li><li class="level-2"><a class="final" href="/install/"><span>Installing Apache PredictionIO (incubating)</span></a></li><li class="level-2"><a class="final" href="/start/download/"><span>Downloading an Engine Template</span></a></li><li class="level-2"><a class="final" href="/start/deploy/"><span>Deploying Your First Engine</span></a></li><li class="level-2"><a class="final" href="/start/customize/"><span>Customizing the Engine</span></a></li></ul></li><li class="level-1 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t/"><span>Batch Predictions</span></a></li><li class="level-2"><a class="final" href="/deploy/monitoring/"><span>Monitoring Engine</span></a></li><li class="level-2"><a class="final" href="/deploy/engineparams/"><span>Setting Engine Parameters</span></a></li><li class="level-2"><a class="final" href="/deploy/enginevariants/"><span>Deploying Multiple Engine Variants</span></a></li><li class="level-2"><a class="final" href="/deploy/plugin/"><span>Engine Server Plugin</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Customizing an Engine</span></a><ul><li class="level-2"><a class="final" href="/customize/"><span>Learning DASE</span></a></li><li class="level-2"><a class="final" href="/customize/dase/"><span>Implement DASE</span></a></li><li class="level-2"><a class="final" href="/customize/troubleshooting/"><span>Troubleshooting Engine Development</span></a></li><li class="level-2"><a class="final" href="/api/current/#package"><span>Engine Scala APIs</sp an></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Collecting and Analyzing Data</span></a><ul><li class="level-2"><a class="final" href="/datacollection/"><span>Event Server Overview</span></a></li><li class="level-2"><a class="final" href="/datacollection/eventapi/"><span>Collecting Data with REST/SDKs</span></a></li><li class="level-2"><a class="final" href="/datacollection/eventmodel/"><span>Events Modeling</span></a></li><li class="level-2"><a class="final" href="/datacollection/webhooks/"><span>Unifying Multichannel Data with Webhooks</span></a></li><li class="level-2"><a class="final" href="/datacollection/channel/"><span>Channel</span></a></li><li class="level-2"><a class="final" href="/datacollection/batchimport/"><span>Importing Data in Batch</span></a></li><li class="level-2"><a class="final" href="/datacollection/analytics/"><span>Using Analytics Tools</span></a></li><li class="level-2"><a class="final" href="/datacollection/plugin/"><span>Eve nt Server Plugin</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Choosing an Algorithm(s)</span></a><ul><li class="level-2"><a class="final" href="/algorithm/"><span>Built-in Algorithm Libraries</span></a></li><li class="level-2"><a class="final" href="/algorithm/switch/"><span>Switching to Another Algorithm</span></a></li><li class="level-2"><a class="final" href="/algorithm/multiple/"><span>Combining Multiple Algorithms</span></a></li><li class="level-2"><a class="final" href="/algorithm/custom/"><span>Adding Your Own Algorithms</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>ML Tuning and Evaluation</span></a><ul><li class="level-2"><a class="final" href="/evaluation/"><span>Overview</span></a></li><li class="level-2"><a class="final" href="/evaluation/paramtuning/"><span>Hyperparameter Tuning</span></a></li><li class="level-2"><a class="final" href="/evaluation/evaluationdashboard/"><span>Evaluation Dashboard</s pan></a></li><li class="level-2"><a class="final" href="/evaluation/metricchoose/"><span>Choosing Evaluation Metrics</span></a></li><li class="level-2"><a class="final" href="/evaluation/metricbuild/"><span>Building Evaluation Metrics</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>System Architecture</span></a><ul><li class="level-2"><a class="final" href="/system/"><span>Architecture Overview</span></a></li><li class="level-2"><a class="final" href="/system/anotherdatastore/"><span>Using Another Data Store</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>PredictionIO Official Templates</span></a><ul><li class="level-2"><a class="final" href="/templates/"><span>Intro</span></a></li><li class="level-2"><a class="expandible" href="#"><span>Recommendation</span></a><ul><li class="level-3"><a class="final" href="/templates/recommendation/quickstart/"><span>Quick Start</span></a></li><li class="level-3"><a class="final" href="/templates/recommendation/dase/"><span>DASE</span></a></li><li class="level-3"><a class="final" href="/templates/recommendation/evaluation/"><span>Evaluation Explained</span></a></li><li class="level-3"><a class="final" href="/templates/recommendation/how-to/"><span>How-To</span></a></li><li class="level-3"><a class="final" href="/templates/recommendation/reading-custom-events/"><span>Read Custom Events</span></a></li><li class="level-3"><a class="final" href="/templates/recommendation/customize-data-prep/"><span>Customize Data Preparator</span></a></li><li class="level-3"><a class="final" href="/templates/recommendation/customize-serving/"><span>Customize Serving</span></a></li><li class="level-3"><a class="final" href="/templates/recommendation/training-with-implicit-preference/"><span>Train with Implicit Preference</span></a></li><li class="level-3"><a class="final" href="/templates/recommendation/blacklist-items/"><span>Filter Recommended Items by Blacklist in Query</span> 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ss="final" href="/templates/similarproduct/quickstart/"><span>Quick Start</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/dase/"><span>DASE</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/how-to/"><span>How-To</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/multi-events-multi-algos/"><span>Multiple Events and Multiple Algorithms</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/return-item-properties/"><span>Returns Item Properties</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/train-with-rate-event/"><span>Train with Rate Event</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/rid-user-set-event/"><span>Get Rid of Events for Users</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/recommended-user/"><span>Recommend Users</span></a></li></ul ></li><li class="level-2"><a class="expandible" >href="#"><span>Classification</span></a><ul><li class="level-3"><a >class="final" href="/templates/classification/quickstart/"><span>Quick >Start</span></a></li><li class="level-3"><a class="final active" >href="/templates/classification/dase/"><span>DASE</span></a></li><li >class="level-3"><a class="final" >href="/templates/classification/how-to/"><span>How-To</span></a></li><li >class="level-3"><a class="final" >href="/templates/classification/add-algorithm/"><span>Use Alternative >Algorithm</span></a></li><li class="level-3"><a class="final" >href="/templates/classification/reading-custom-properties/"><span>Read Custom >Properties</span></a></li></ul></li></ul></li><li class="level-1"><a >class="expandible" href="#"><span>Engine Template Gallery</span></a><ul><li >class="level-2"><a class="final" >href="/gallery/template-gallery/"><span>Browse</span></a></li><li >class="level-2"><a class="final" >href="/community/submit-template/"><span>Submit you r Engine as a Template</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Demo Tutorials</span></a><ul><li class="level-2"><a class="final" href="/demo/tapster/"><span>Comics Recommendation Demo</span></a></li><li class="level-2"><a class="final" href="/demo/community/"><span>Community Contributed Demo</span></a></li><li class="level-2"><a class="final" href="/demo/textclassification/"><span>Text Classification Engine Tutorial</span></a></li></ul></li><li class="level-1"><a class="expandible" href="/community/"><span>Getting Involved</span></a><ul><li class="level-2"><a class="final" href="/community/contribute-code/"><span>Contribute Code</span></a></li><li class="level-2"><a class="final" href="/community/contribute-documentation/"><span>Contribute Documentation</span></a></li><li class="level-2"><a class="final" href="/community/contribute-sdk/"><span>Contribute a SDK</span></a></li><li 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Instructions</span></a></li><li class="level-2"><a class="final" href="/resources/glossary/ "><span>Glossary</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Apache Software Foundation</span></a><ul><li class="level-2"><a class="final" href="https://www.apache.org/";><span>Apache Homepage</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/licenses/";><span>License</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/foundation/sponsorship.html";><span>Sponsorship</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/foundation/thanks.html";><span>Thanks</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/security/";><span>Security</span></a></li></ul></li></ul></nav></div><div class="col-md-9 col-sm-12"><div class="content-header hidden-md hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a href="#">PredictionIO Official Templates</a><span class="spacer">></span></li><li><a href="#">Classification</a><span clas s="spacer">></span></li><li><span class="last">DASE</span></li></ul></div><div id="page-title"><h1>DASE Components Explained (Classification)</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/classification/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="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a href="#">PredictionIO Official Templates</a><span class="spacer">></span></li><li><a href="#">Classification</a><span class="spacer">></span></li><li><span class="last">DASE</span></li></ul></div>< div id="page-title"><h1>DASE Components Explained (Classification)</h1></div></div><div class="content"> <p>PredictionIO'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's look at the code and see how you can customize the engine you built from the Classification Engine Template.</p><div class="alert-message note"><p>Evaluator will not be covered in this tutorial. Please visit <a href="/evaluation/paramtuning/">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>MyClassification</em> takes a JSON prediction query, e.g. <code>{ "attr0&q uot;:4, "attr1":3, "attr2":8 }</code>, and return a JSON predicted result.</p><div class="alert-message warning"><p>for version < v0.3.1, it is array of features values: <code>{ "features": [4, 3, 8] }</code></p></div><p>In MyClassification/src/main/scala/<strong><em>Engine.scala</em></strong>, the <code>Query</code> case class defines the format of <strong>query</strong>, such as <code>{ "attr0":4, "attr1":3, "attr2":8 }</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 -2 -3 -4 -5 -6</pre></td><td class="code"><pre><span class="k">case</span> <span class="k">class</span> <span class="nc">Query</span><span class="o">(</span> - <span class="n">attr0</span> <span class="k">:</span> <span class="kt">Double</span><span class="o">,</span> - <span class="n">attr1</span> <span class="k">:</span> <span class="kt">Double</span><span class="o">,</span> - <span class="n">attr2</span> <span class="k">:</span> <span class="kt">Double</span> -<span class="o">)</span> - -</pre></td></tr></tbody></table> </div> <p>The <code>PredictedResult</code> case class defines the format of <strong>predicted result</strong>, such as <code>{"label":2.0}</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 -2 -3</pre></td><td class="code"><pre><span class="k">case</span> <span class="k">class</span> <span class="nc">PredictedResult</span><span class="o">(</span> - <span class="k">val</span> <span class="n">label</span><span class="k">:</span> <span class="kt">Double</span> -<span class="o">)</span> -</pre></td></tr></tbody></table> </div> <p>Finally, <code>ClassificationEngine</code> is the Engine Factory 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 -2 -3 -4 -5 -6 -7 -8 -9</pre></td><td class="code"><pre><span class="k">object</span> <span class="nc">ClassificationEngine</span> <span class="k">extends</span> <span class="nc">IEngineFactory</span> <span class="o">{</span> - <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">"naive"</span> <span class="o">-></span> <span class="n">classOf</span><span class="o">[</span><span class="kt">NaiveBayesAlgorithm</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> -</pre></td></tr></tbody></table> </div> <h3 id='spark-mllib' class='header-anchors'>Spark MLlib</h3><p>Spark's MLlib NaiveBayes algorithm takes training data of RDD type, i.e. <code>RDD[LabeledPoint]</code> and train a model, which is a <code>NaiveBayesModel</code> object.</p><p>PredictionIO's MLlib Classification engine template, which <em>MyClassification</em> bases on, integrates this algorithm under the DASE architecture. We will take a closer look at the DASE code below.</p> <blockquote> <p><a href="https://spark.apache.org/docs/latest/mllib-naive-bayes.html";>Check this out</a> to learn more about MLlib's NaiveBayes algorithm.</p></blockquote> <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[LabeledPoint]</code> for the NaiveBayes algorithm.< /p><h3 id='data-source' class='header-anchors'>Data Source</h3><p>In MyClassification/src/main/scala/<strong><em>DataSource.scala</em></strong>, the <code>readTraining</code> method of the class <code>DataSource</code> reads, and selects, data from datastore of EventServer and it 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 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 -13 -14 -15 -16 -17 -18 -19 -20 -21 -22 -23 -24 -25 -26 -27 -28 -29 -30 -31 -32 -33 -34 -35 -36 -37 -38</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">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">val</span> <span class="n">labeledPoints</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">LabeledPoint</span><span class="o">]</span> <span class="k">=</span> <span class="nc">PEventStore</span><span class="o">.</span><span class="n">aggregateProperties</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="s">"user"</span><span class="o">,</span> - <span class="c1">// only keep entities with these required properties defined -</span> <span class="n">required</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">"plan"</span><span class="o">,</span> <span class="s">"attr0"</span><span class="o">,</span> <span class="s">"attr1"</span><span class="o">,</span> <span class="s">"attr2"</span><span class="o">)))(</span><span class="n">sc</span><span class="o">)</span> - <span class="c1">// aggregateProperties() returns RDD pair of -</span> <span class="c1">// entity ID and its aggregated properties -</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">entityId</span><span class="o">,</span> <span class="n">properties</span><span class="o">)</span> <span class="k">=></span> - <span class="k">try</span> <span class="o">{</span> - <span class="nc">LabeledPoint</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">"plan"</span><span class="o">),</span> - <span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span><span class="nc">Array</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">"attr0"</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">"attr1"</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">"attr2"</span><span class="o">)</span> - <span class="o">))</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">=></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">"Failed to get properties ${properties} of"</span> <span class="o">+</span> - <span class="n">s</span><span class="s">" ${entityId}. 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="o">}.</span><span class="n">cache</span><span class="o">()</span> - - <span class="k">new</span> <span class="nc">TrainingData</span><span class="o">(</span><span class="n">labeledPoints</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 through the <em>Event Server</em>, and <code>PEventStore.aggregateProperties</code> aggregates the event records of the 4 properties (attr0, attr1, attr2 and plan) for each user.</p><p>PredictionIO automatically loads the parameters of <em>datasource</em> specified in MyEngine/<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 -2 -3 -4 -5 -6 -7 -8 -9</pre></td><td class="code"><pre><span class="o">{</span> - ... - <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>In this sample text data file, columns are delimited by comma (,). The first column are labels. The second column are features.</p><p>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 -2 -3</pre></td><td class="code"><pre><span class="k">class</span> <span class="nc">TrainingData</span><span class="o">(</span> - <span class="k">val</span> <span class="n">labeledPoints</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">LabeledPoint</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>and PredictionIO passes the returned <code>TrainingData</code> object to <em>Data Preparator</em>.</p><h3 id='data-preparator' class='header-anchors'>Data Preparator</h3><p>In MyClassification/src/main/scala/<strong><em>Preparator.scala</em></strong>, the <code>prepare</code> of class <code>Preparator</code> takes <code>TrainingData</code>. It then conducts 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 NaiveBayes, it is <code>RDD[LabeledPoint]</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 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11</pre></td><td class="code"><pre><span class="k">class</span> <span class="nc">PreparedData</span><span class="o">(</span> - <span class="k">val</span> <span class="n">labeledPoints</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">LabeledPoint</span><span class="o">]</span> -<span class="o">)</span> <span class="k">extends</span> <span class="nc">Serializable</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="k">new</span> <span class="nc">PreparedData</span><span class="o">(</span><span class="n">trainingData</span><span class="o">.</span><span class="n">labeledPoints</span><span class="o">)</span> - <span class="o">}</span> -<span class="o">}</span> -</pre></td></tr></tbody></table> </div> <p>PredictionIO passes the returned <code>PreparedData</code> object to Algorithm's <code>train</code> function.</p><h2 id='algorithm' class='header-anchors'>Algorithm</h2><p>In MyClassification/src/main/scala/<strong><em>NaiveBayesAlgorithm.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 NaiveBayes algorithm, i.e. <code>NaiveBayes.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 -2 -3</pre></td><td class="code"><pre><span class="k">def</span> <span class="n">train</span><span class="o">(</span><span class="n">sc</span><span class="k">:</span> <span class="kt">SparkContext</span><span class="o">,</span> <span class="n">data</span><span class="k">:</span> <span class="kt">PreparedData</span><span class="o">)</span><span class="k">:</span> <span class="kt">NaiveBayesModel</span> <span class="o">=</span> <span class="o">{</span> - <span class="nc">NaiveBayes</span><span class="o">.</span><span class="n">train</span><span class="o">(</span><span class="n">data</span><span class="o">.</span><span class="n">labeledPoints</span><span class="o">,</span> <span class="n">ap</span><span class="o">.</span><span class="n">lambda</span><span class="o">)</span> -<span class="o">}</span> -</pre></td></tr></tbody></table> </div> <p>In addition to <code>RDD[LabeledPoint]</code> (i.e. <code>data.labeledPoints</code>), <code>NaiveBayes.train</code> takes 1 parameter: <em>lambda</em>.</p><p>The values of this parameter is specified in <em>algorithms</em> of MyClassification/<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 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12</pre></td><td class="code"><pre><span class="o">{</span> - ... - <span class="s2">"algorithms"</span>: <span class="o">[</span> - <span class="o">{</span> - <span class="s2">"name"</span>: <span class="s2">"naive"</span>, - <span class="s2">"params"</span>: <span class="o">{</span> - <span class="s2">"lambda"</span>: 1.0 - <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 class <code>AlgorithmParams</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 -2 -3</pre></td><td class="code"><pre><span class="k">case</span> <span class="k">class</span> <span class="nc">AlgorithmParams</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="k">extends</span> <span class="nc">Params</span> -</pre></td></tr></tbody></table> </div> <p><code>NaiveBayes.train</code> then returns a <code>NaiveBayesModel</code> model. PredictionIO will automatically store the returned model.</p><h3 id='predict(...)' class='header-anchors'>predict(...)</h3><p>The <code>predict</code> method 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>{ "attr0":4, "attr1":3, "attr2":8 }</code> to the <code>Query</code> class you defined previously.</p><p>The predictive model <code>NaiveBayesModel</code> of MLlib NaiveBayes offers a function called <code>predict</code>. <code>predict</code> takes a dense vector of features. It predicts the label of the item represented by this feature vector.</p><div class="highlight scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1 -2 -3 -4 -5 -6</pre></td><td class="code"><pre> <span class="k">def</span> <span class="n">predict</span><span class="o">(</span><span class="n">model</span><span class="k">:</span> <span class="kt">NaiveBayesModel</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="k">:</span> <span class="kt">PredictedResult</span> <span class="o">=</span> <span class="o">{</span> - <span class="k">val</span> <span class="n">label</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="o">(</span><span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span> - <span class="n">query</span><span class="o">.</span><span class="n">attr0</span><span class="o">,</span> <span class="n">query</span><span class="o">.</span><span class="n">attr1</span><span class="o">,</span> <span class="n">query</span><span class="o">.</span><span class="n">attr2</span> - <span class="o">))</span> - <span class="nc">PredictedResult</span><span class="o">(</span><span class="n">label</span><span class="o">)</span> - <span class="o">}</span> -</pre></td></tr></tbody></table> </div> <blockquote> <p>You have defined the class <code>PredictedResult</code> earlier in this page.</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 MyClassification/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 -2 -3 -4 -5 -6 -7 -8 -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>NaiveBayesAlgorithm</code> is implemented by default, this <code>Seq</code> contains one element.</p></blockquote> <p>In this case, <code>serve</code> simply returns the predicted result of the first, and the only, algorithm, i.e. <code>predictedResults.head</code>.</p><p>Congratulations! You have just learned how to customize and build a production-ready engine. 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