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From don...@apache.org
Subject [07/51] [abbrv] [partial] incubator-predictionio-site git commit: Documentation based on apache/incubator-predictionio#1d8f19b91cb0926ccffc872a906e262050091c64
Date Wed, 02 Nov 2016 22:17:07 GMT
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+<!DOCTYPE html><html><head><title>DASE Components Explained (Lead Scoring)</title><meta charset="utf-8"/><meta content="IE=edge,chrome=1" http-equiv="X-UA-Compatible"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><meta class="swiftype" name="title" data-type="string" content="DASE Components Explained (Lead Scoring)"/><link rel="canonical" href="https://docs.prediction.io/templates/leadscoring/dase/"/><link href="/images/favicon/normal-b330020a.png" rel="shortcut icon"/><link href="/images/favicon/apple-c0febcf2.png" rel="apple-touch-icon"/><link href="//fonts.googleapis.com/css?family=Open+Sans:300italic,400italic,600italic,700italic,800italic,400,300,600,700,800" rel="stylesheet"/><link href="//maxcdn.bootstrapcdn.com/font-awesome/4.2.0/css/font-awesome.min.css" rel="stylesheet"/><link href="/stylesheets/application-a2a2f408.css" rel="stylesheet" type="text/css"/><script src="//cdnjs.cloudflare.com/ajax/libs/html5shiv/3.7.2/html5shiv.min.js"></script><scr
 ipt src="//cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><script src="//use.typekit.net/pqo0itb.js"></script><script>try{Typekit.load({ async: true });}catch(e){}</script></head><body><div id="global"><header><div class="container" id="header-wrapper"><div class="row"><div class="col-sm-12"><div id="logo-wrapper"><span id="drawer-toggle"></span><a href="#"></a><a href="http://predictionio.incubator.apache.org/"><img alt="PredictionIO" id="logo" src="/images/logos/logo-ee2b9bb3.png"/></a></div><div id="menu-wrapper"><div id="pill-wrapper"><a class="pill left" href="/gallery/template-gallery">TEMPLATES</a> <a class="pill right" href="//github.com/apache/incubator-predictionio/">OPEN SOURCE</a></div></div><img class="mobile-search-bar-toggler hidden-md hidden-lg" src="/images/icons/search-glass-704bd4ff.png"/></div></div></div></header><div id="search-bar-row-wrapper"><div class="container-fluid" id="search-bar-row"><div class="row"><div class="col-md
 -9 col-sm-11 col-xs-11"><div class="hidden-md hidden-lg" id="mobile-page-heading-wrapper"><p>PredictionIO Docs</p><h4>DASE Components Explained (Lead Scoring)</h4></div><h4 class="hidden-sm hidden-xs">PredictionIO Docs</h4></div><div class="col-md-3 col-sm-1 col-xs-1 hidden-md hidden-lg"><img id="left-menu-indicator" src="/images/icons/down-arrow-dfe9f7fe.png"/></div><div class="col-md-3 col-sm-12 col-xs-12 swiftype-wrapper"><div class="swiftype"><form class="search-form"><img class="search-box-toggler hidden-xs hidden-sm" src="/images/icons/search-glass-704bd4ff.png"/><div class="search-box"><img src="/images/icons/search-glass-704bd4ff.png"/><input type="text" id="st-search-input" class="st-search-input" placeholder="Search Doc..."/></div><img class="swiftype-row-hider hidden-md hidden-lg" src="/images/icons/drawer-toggle-active-fcbef12a.png"/></form></div></div><div class="mobile-left-menu-toggler hidden-md hidden-lg"></div></div></div></div><div id="page" 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"><a class="expandible" 
 href="#"><span>Integrating with Your App</span></a><ul><li class="level-2"><a class="final" href="/appintegration/"><span>App Integration Overview</span></a></li><li class="level-2"><a class="expandible" href="/sdk/"><span>List of SDKs</span></a><ul><li class="level-3"><a class="final" href="/sdk/java/"><span>Java & Android SDK</span></a></li><li class="level-3"><a class="final" href="/sdk/php/"><span>PHP SDK</span></a></li><li class="level-3"><a class="final" href="/sdk/python/"><span>Python SDK</span></a></li><li class="level-3"><a class="final" href="/sdk/ruby/"><span>Ruby SDK</span></a></li><li class="level-3"><a class="final" href="/sdk/community/"><span>Community Powered SDKs</span></a></li></ul></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Deploying an Engine</span></a><ul><li class="level-2"><a class="final" href="/deploy/"><span>Deploying as a Web Service</span></a></li><li class="level-2"><a class="final" href="/cli/#engine-commands"><span>Engine 
 Command-line Interface</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></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</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Collecting and Analyzing Dat
 a</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="/cli/#event-server-commands"><span>Event Server Command-line Interface</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></ul></li><li class="level-1"><a class="expandible" hre
 f="#"><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</span></a></li><li class="level-2"><a class="final" href="/evaluation/metricchoose/"><spa
 n>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>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 your 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</sp
 an></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 class="level-2"><a class="final" href="/community/contribute-webhook/"><span>Contribute a Webhook</span></a></li><li class="level-2"><a class="final" href="/community/projects/"><span>Community Projects</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Getting
  Help</span></a><ul><li class="level-2"><a class="final" href="/resources/faq/"><span>FAQs</span></a></li><li class="level-2"><a class="final" href="/support/"><span>Support</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Resources</span></a><ul><li class="level-2"><a class="final" href="/resources/intellij/"><span>Developing Engines with IntelliJ IDEA</span></a></li><li class="level-2"><a class="final" href="/resources/upgrade/"><span>Upgrade Instructions</span></a></li><li class="level-2"><a class="final" href="/resources/glossary/"><span>Glossary</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="page-title"><h1>DASE Components Explained (Lead Scoring)</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/leadscoring/dase.html.md.erb"><img src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div class="content-header hidden-sm hidden-xs"><div id="page-title"><h1>DASE Components Explained (Lead Scoring)</h1></div></div><div class="content"><p>PredictionIO&#39;s DASE architecture brings the separation-of-concerns design principle to predictive engine development. DASE stands for the following components of an engine:</p> <ul> <li><strong>D</strong>ata - includes Data Source and Data Preparator</li> <li><strong>A</strong>lgorithm(s)</li> <li><strong>S</strong>erving</li> <li><strong>E</strong>valuator</li> </ul> <p><p>Let&#39;s look at the code and see how you can customize the engine you built from the Lead Scoring Engine Template.</p><div class="alert-messag
 e note"><p>Evaluator will not be covered in this tutorial.</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>MyLeadScoring</em> takes a JSON prediction query, e.g. &#39;{ &quot;landingPageId&quot; : &quot;example.com/page9&quot;, &quot;referrerId&quot; : &quot;referrer10.com&quot;, &quot;browser&quot;: &quot;Firefox&quot; }&#39; , and return a JSON predicted result. In MyLeadScoring/src/main/scala/<strong><em>Engine.scala</em></strong>, the <code>Query</code> case class defines the format of such <strong>query</strong>:</p><div class="highlight scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
+2
+3
+4
+5</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">landingPageId</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span>
+  <span class="n">referrerId</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span>
+  <span class="n">browser</span><span class="k">:</span> <span class="kt">String</span>
+<span class="o">)</span> <span class="k">extends</span> <span class="nc">Serializable</span>
+</pre></td></tr></tbody></table> </div> <p>The <code>PredictedResult</code> case class defines the format of <strong>predicted result</strong>, such as</p><div class="highlight json"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td class="code"><pre><span class="p">{</span><span class="s2">"score"</span><span class="p">:</span><span class="mf">0.7466666666666667</span><span class="p">}</span><span class="w">
+</span></pre></td></tr></tbody></table> </div> <p>with:</p><div class="highlight scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
+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="n">score</span><span class="k">:</span> <span class="kt">Double</span>
+<span class="o">)</span> <span class="k">extends</span> <span class="nc">Serializable</span>
+</pre></td></tr></tbody></table> </div> <p>Finally, <code>LeadScoringEngine</code> is the <em>Engine Factory</em> that defines the components this engine will use: Data Source, Data Preparator, Algorithm(s) and Serving components.</p><div class="highlight scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
+2
+3
+4
+5
+6
+7
+8
+9</pre></td><td class="code"><pre><span class="k">object</span> <span class="nc">LeadScoringEngine</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">"randomforest"</span> <span class="o">-&gt;</span> <span class="n">classOf</span><span class="o">[</span><span class="kt">RFAlgorithm</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> <p>Each DASE component of the <code>LeadScoringEngine</code> will be explained below.</p><p>By default, Spark&#39;s MLlib <a href="https://spark.apache.org/docs/latest/mllib-ensembles.html#random-forests">RandomForest algorithm</a> is used.</p><h2 id='data' class='header-anchors'>Data</h2><p>In the DASE architecture, data is prepared by 2 components sequentially: <em>DataSource</em> and <em>DataPreparator</em>. They take data from the data store and prepare them for Algorithm.</p><h3 id='data-source' class='header-anchors'>Data Source</h3><p>In MyLeadScoring/src/main/scala/<strong><em>DataSource.scala</em></strong>, the <code>readTraining</code> method of class <code>DataSource</code> reads and selects data from the <em>Event Store</em> (data store of the <em>Event Server</em>). 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
+39
+40
+41
+42
+43
+44
+45
+46
+47
+48
+49
+50
+51
+52
+53
+54
+55
+56
+57
+58
+59
+60
+61
+62
+63
+64
+65
+66
+67
+68
+69
+70
+71
+72
+73
+74
+75</pre></td><td class="code"><pre><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">viewPage</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[(</span><span class="kt">String</span>, <span class="kt">Event</span><span class="o">)]</span> <span class="k">=</span> <span class="nc">PEventStore</span><span class="o">.</span><span class="n">find</span><span class="o">(</span>
+      <span class="n">appName</span> <span class="k">=</span> <span class="n">dsp</span><span class="o">.</span><span class="n">appName</span><span class="o">,</span>
+      <span class="n">entityType</span> <span class="k">=</span> <span class="nc">Some</span><span class="o">(</span><span class="s">"user"</span><span class="o">),</span>
+      <span class="n">eventNames</span> <span class="k">=</span> <span class="nc">Some</span><span class="o">(</span><span class="nc">Seq</span><span class="o">(</span><span class="s">"view"</span><span class="o">)),</span>
+      <span class="c1">// targetEntityType is optional field of an event.
+</span>      <span class="n">targetEntityType</span> <span class="k">=</span> <span class="nc">Some</span><span class="o">(</span><span class="nc">Some</span><span class="o">(</span><span class="s">"page"</span><span class="o">)))(</span><span class="n">sc</span><span class="o">)</span>
+      <span class="c1">// PEventStore.find() returns RDD[Event]
+</span>      <span class="o">.</span><span class="n">map</span> <span class="o">{</span> <span class="n">event</span> <span class="k">=&gt;</span>
+        <span class="k">val</span> <span class="n">sessionId</span> <span class="k">=</span> <span class="k">try</span> <span class="o">{</span>
+          <span class="n">event</span><span class="o">.</span><span class="n">properties</span><span class="o">.</span><span class="n">get</span><span class="o">[</span><span class="kt">String</span><span class="o">](</span><span class="s">"sessionId"</span><span class="o">)</span>
+        <span class="o">}</span> <span class="k">catch</span> <span class="o">{</span>
+          <span class="k">case</span> <span class="n">e</span><span class="k">:</span> <span class="kt">Exception</span> <span class="o">=&gt;</span> <span class="o">{</span>
+            <span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="o">(</span><span class="n">s</span><span class="s">"Cannot get sessionId from event ${event}. ${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">sessionId</span><span class="o">,</span> <span class="n">event</span><span class="o">)</span>
+      <span class="o">}</span>
+
+    <span class="k">val</span> <span class="n">buyItem</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[(</span><span class="kt">String</span>, <span class="kt">Event</span><span class="o">)]</span> <span class="k">=</span> <span class="nc">PEventStore</span><span class="o">.</span><span class="n">find</span><span class="o">(</span>
+      <span class="n">appName</span> <span class="k">=</span> <span class="n">dsp</span><span class="o">.</span><span class="n">appName</span><span class="o">,</span>
+      <span class="n">entityType</span> <span class="k">=</span> <span class="nc">Some</span><span class="o">(</span><span class="s">"user"</span><span class="o">),</span>
+      <span class="n">eventNames</span> <span class="k">=</span> <span class="nc">Some</span><span class="o">(</span><span class="nc">Seq</span><span class="o">(</span><span class="s">"buy"</span><span class="o">)),</span>
+      <span class="c1">// targetEntityType is optional field of an event.
+</span>      <span class="n">targetEntityType</span> <span class="k">=</span> <span class="nc">Some</span><span class="o">(</span><span class="nc">Some</span><span class="o">(</span><span class="s">"item"</span><span class="o">)))(</span><span class="n">sc</span><span class="o">)</span>
+      <span class="c1">// PEventStore.find() returns RDD[Event]
+</span>      <span class="o">.</span><span class="n">map</span> <span class="o">{</span> <span class="n">event</span> <span class="k">=&gt;</span>
+        <span class="k">val</span> <span class="n">sessionId</span> <span class="k">=</span> <span class="k">try</span> <span class="o">{</span>
+          <span class="n">event</span><span class="o">.</span><span class="n">properties</span><span class="o">.</span><span class="n">get</span><span class="o">[</span><span class="kt">String</span><span class="o">](</span><span class="s">"sessionId"</span><span class="o">)</span>
+        <span class="o">}</span> <span class="k">catch</span> <span class="o">{</span>
+          <span class="k">case</span> <span class="n">e</span><span class="k">:</span> <span class="kt">Exception</span> <span class="o">=&gt;</span> <span class="o">{</span>
+            <span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="o">(</span><span class="n">s</span><span class="s">"Cannot get sessionId from event ${event}. ${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">sessionId</span><span class="o">,</span> <span class="n">event</span><span class="o">)</span>
+      <span class="o">}</span>
+
+    <span class="k">val</span> <span class="n">session</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">Session</span><span class="o">]</span> <span class="k">=</span> <span class="n">viewPage</span><span class="o">.</span><span class="n">cogroup</span><span class="o">(</span><span class="n">buyItem</span><span class="o">)</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">sessionId</span><span class="o">,</span> <span class="o">(</span><span class="n">viewIter</span><span class="o">,</span> <span class="n">buyIter</span><span class="o">))</span> <span class="k">=&gt;</span>
+        <span class="c1">// the first view event of the session is the landing event
+</span>        <span class="k">val</span> <span class="n">landing</span> <span class="k">=</span> <span class="n">viewIter</span><span class="o">.</span><span class="n">reduce</span><span class="o">{</span> <span class="o">(</span><span class="n">a</span><span class="o">,</span> <span class="n">b</span><span class="o">)</span> <span class="k">=&gt;</span>
+          <span class="k">if</span> <span class="o">(</span><span class="n">a</span><span class="o">.</span><span class="n">eventTime</span><span class="o">.</span><span class="n">isBefore</span><span class="o">(</span><span class="n">b</span><span class="o">.</span><span class="n">eventTime</span><span class="o">))</span> <span class="n">a</span> <span class="k">else</span> <span class="n">b</span>
+        <span class="o">}</span>
+        <span class="c1">// any buy after landing
+</span>        <span class="k">val</span> <span class="n">buy</span> <span class="k">=</span> <span class="n">buyIter</span><span class="o">.</span><span class="n">filter</span><span class="o">(</span> <span class="n">b</span> <span class="k">=&gt;</span> <span class="n">b</span><span class="o">.</span><span class="n">eventTime</span><span class="o">.</span><span class="n">isAfter</span><span class="o">(</span><span class="n">landing</span><span class="o">.</span><span class="n">eventTime</span><span class="o">))</span>
+          <span class="o">.</span><span class="n">nonEmpty</span>
+
+        <span class="k">try</span> <span class="o">{</span>
+          <span class="k">new</span> <span class="nc">Session</span><span class="o">(</span>
+            <span class="n">landingPageId</span> <span class="k">=</span> <span class="n">landing</span><span class="o">.</span><span class="n">targetEntityId</span><span class="o">.</span><span class="n">get</span><span class="o">,</span>
+            <span class="n">referrerId</span> <span class="k">=</span> <span class="n">landing</span><span class="o">.</span><span class="n">properties</span><span class="o">.</span><span class="n">getOrElse</span><span class="o">[</span><span class="kt">String</span><span class="o">](</span><span class="s">"referrerId"</span><span class="o">,</span> <span class="s">""</span><span class="o">),</span>
+            <span class="n">browser</span> <span class="k">=</span> <span class="n">landing</span><span class="o">.</span><span class="n">properties</span><span class="o">.</span><span class="n">getOrElse</span><span class="o">[</span><span class="kt">String</span><span class="o">](</span><span class="s">"browser"</span><span class="o">,</span> <span class="s">""</span><span class="o">),</span>
+            <span class="n">buy</span> <span class="k">=</span> <span class="n">buy</span>
+          <span class="o">)</span>
+        <span class="o">}</span> <span class="k">catch</span> <span class="o">{</span>
+          <span class="k">case</span> <span class="n">e</span><span class="k">:</span> <span class="kt">Exception</span> <span class="o">=&gt;</span> <span class="o">{</span>
+            <span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="o">(</span><span class="n">s</span><span class="s">"Cannot create session data from ${landing}. ${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">session</span><span class="o">)</span>
+  <span class="o">}</span>
+<span class="o">}</span>
+</pre></td></tr></tbody></table> </div> <p>PredictionIO automatically loads the parameters of <em>datasource</em> specified in MyLeadScoring/<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 <code>readTraining()</code>, <code>PEventStore</code> is an object which provides function to access data that is collected by PredictionIO Event Server.</p><p>This Lead Scoring Engine Template requires &quot;view&quot; and &quot;buy&quot; events with <code>sessionId</code> in event property.</p><p><code>PEventStore.find(...)</code> specifies the events that you want to read. In this case, &quot;user view page&quot; and &quot;user buy item&quot; events are read and then each is mapped to tuple of (sessionId, event). The event are then &quot;cogrouped&quot; by sessionId to find out the information in the session, such as first page view (landing page view), and whether the user converts (buy event), to craete a RDD of Session as TrainingData:</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">case</span> <span class="k">class</span> <span class="nc">Session</span><span class="o">(</span>
+  <span class="n">landingPageId</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span>
+  <span class="n">referrerId</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span>
+  <span class="n">browser</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span>
+  <span class="n">buy</span><span class="k">:</span> <span class="kt">Boolean</span> <span class="c1">// buy or not
+</span><span class="o">)</span> <span class="k">extends</span> <span class="nc">Serializable</span>
+
+<span class="k">class</span> <span class="nc">TrainingData</span><span class="o">(</span>
+  <span class="k">val</span> <span class="n">session</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">Session</span><span class="o">]</span>
+<span class="o">)</span> <span class="k">extends</span> <span class="nc">Serializable</span>
+
+</pre></td></tr></tbody></table> </div> <p>PredictionIO then passes the returned <code>TrainingData</code> object to <em>Data Preparator</em>.</p><div class="alert-message note"><p>You could modify the DataSource to read other event other than the default <strong>buy</strong> if the definition of conversion is not &quot;buy item&quot; event.</p></div><h3 id='data-preparator' class='header-anchors'>Data Preparator</h3><p>In MyLeadScoring/src/main/scala/<strong><em>Preparator.scala</em></strong>, the <code>prepare</code> method of class <code>Preparator</code> takes <code>TrainingData</code> as its input and performs any necessary feature selection and data processing tasks. At the end, it returns <code>PreparedData</code> which should contain the data <em>Algorithm</em> needs.</p><p>In this template, <code>prepare</code> will select the features from the Session object and convert them to the data required by the MLlib&#39;s RandomForest algorithm.</p><p>The <code>PreparedData</code>
  is defined as:</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</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="k">val</span> <span class="n">featureIndex</span><span class="k">:</span> <span class="kt">Map</span><span class="o">[</span><span class="kt">String</span>, <span class="kt">Int</span><span class="o">],</span>
+  <span class="k">val</span> <span class="n">featureCategoricalIntMap</span><span class="k">:</span> <span class="kt">Map</span><span class="o">[</span><span class="kt">String</span>, <span class="kt">Map</span><span class="o">[</span><span class="kt">String</span>, <span class="kt">Int</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>The <code>LabeledPoint</code> class is defined in Spark MLlib and it&#39;s required for the RandomForest Algorithm. The <code>featureIndex</code> is a Map of feature name to the position index in the feature vector. <code>featureCategoricalIntMap</code> is a Map of categorical feature name to the Map of categorical value map for this feature.</p><p>By default, the feature used for classification is &quot;landingPage&quot;, &quot;referrer&quot; and &quot;browser&quot;. Since these features contain categorical values, we need to create a map of categorical values to the integer values for the algorithm to use.</p><div class="alert-message note"><p>You can customize the tempate to use other features.</p></div><p>For example, if the feature &quot;landingPage&quot; can be any of the following values: &quot;page1&quot;, &quot;page2&quot;, &quot;page3&quot;, &quot;page4&quot;. We can create a categorical Int value Map, such as:</p><div class="high
 light 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="nc">Map</span><span class="o">(</span>
+  <span class="s">"page1"</span> <span class="o">-&gt;</span> <span class="mi">0</span><span class="o">,</span>
+  <span class="s">"page2"</span> <span class="o">-&gt;</span> <span class="mi">1</span><span class="o">,</span>
+  <span class="s">"page3"</span> <span class="o">-&gt;</span> <span class="mi">2</span><span class="o">,</span>
+  <span class="s">"page4"</span> <span class="o">-&gt;</span> <span class="mi">3</span>
+<span class="o">)</span>
+</pre></td></tr></tbody></table> </div> <p>Instead of manually create such Map, a helper method <code>createCategoricalIntMap()</code> is defined in <strong>Prepraator.scala</strong> for this purpose.</p><p>Each <code>labeledPoint</code> is a label and a feature vector. The element index of the vector for the coresponding feature is defined by <code>featureIndex</code> Map. By default, it&#39;s defined as</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</pre></td><td class="code"><pre><span class="k">val</span> <span class="n">featureIndex</span> <span class="k">=</span> <span class="nc">Map</span><span class="o">(</span>
+  <span class="s">"landingPage"</span> <span class="o">-&gt;</span> <span class="mi">0</span><span class="o">,</span>
+  <span class="s">"referrer"</span> <span class="o">-&gt;</span> <span class="mi">1</span><span class="o">,</span>
+  <span class="s">"browser"</span> <span class="o">-&gt;</span> <span class="mi">2</span>
+<span class="o">)</span>
+</pre></td></tr></tbody></table> </div> <p>which means that index 0 of the feature vector is the &quot;landingPage&quot; feature, index 1 is &quot;referrer&quot; feature, and so on.</p><p>The <code>prepare()</code> of the <code>Preparator</code> class first finds out all possible categorical values for the features and create a categorical Int map. Then it converts to the <code>Session</code> object to the <code>LabeledPoint</code> by creating the feature vector and the label. In this case, the label is 1 if there is any conversion and 0 if there is no conversion:</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
+39
+40
+41
+42
+43
+44
+45
+46
+47
+48
+49
+50
+51
+52
+53
+54
+55
+56
+57
+58
+59
+60
+61
+62
+63
+64</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="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">td</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">// find out all values of the each feature
+</span>    <span class="k">val</span> <span class="n">landingValues</span> <span class="k">=</span> <span class="n">td</span><span class="o">.</span><span class="n">session</span><span class="o">.</span><span class="n">map</span><span class="o">(</span><span class="k">_</span><span class="o">.</span><span class="n">landingPageId</span><span class="o">).</span><span class="n">distinct</span><span class="o">.</span><span class="n">collect</span>
+    <span class="k">val</span> <span class="n">referrerValues</span> <span class="k">=</span> <span class="n">td</span><span class="o">.</span><span class="n">session</span><span class="o">.</span><span class="n">map</span><span class="o">(</span><span class="k">_</span><span class="o">.</span><span class="n">referrerId</span><span class="o">).</span><span class="n">distinct</span><span class="o">.</span><span class="n">collect</span>
+    <span class="k">val</span> <span class="n">browserValues</span> <span class="k">=</span> <span class="n">td</span><span class="o">.</span><span class="n">session</span><span class="o">.</span><span class="n">map</span><span class="o">(</span><span class="k">_</span><span class="o">.</span><span class="n">browser</span><span class="o">).</span><span class="n">distinct</span><span class="o">.</span><span class="n">collect</span>
+
+    <span class="c1">// map feature value to integer for each categorical feature
+</span>    <span class="k">val</span> <span class="n">featureCategoricalIntMap</span> <span class="k">=</span> <span class="nc">Map</span><span class="o">(</span>
+      <span class="s">"landingPage"</span> <span class="o">-&gt;</span> <span class="n">createCategoricalIntMap</span><span class="o">(</span><span class="n">landingValues</span><span class="o">,</span> <span class="s">""</span><span class="o">),</span>
+      <span class="s">"referrer"</span> <span class="o">-&gt;</span> <span class="n">createCategoricalIntMap</span><span class="o">(</span><span class="n">referrerValues</span><span class="o">,</span> <span class="s">""</span><span class="o">),</span>
+      <span class="s">"browser"</span> <span class="o">-&gt;</span> <span class="n">createCategoricalIntMap</span><span class="o">(</span><span class="n">browserValues</span><span class="o">,</span> <span class="s">""</span><span class="o">)</span>
+    <span class="o">)</span>
+    <span class="c1">// index position of each feature in the vector
+</span>    <span class="k">val</span> <span class="n">featureIndex</span> <span class="k">=</span> <span class="nc">Map</span><span class="o">(</span>
+      <span class="s">"landingPage"</span> <span class="o">-&gt;</span> <span class="mi">0</span><span class="o">,</span>
+      <span class="s">"referrer"</span> <span class="o">-&gt;</span> <span class="mi">1</span><span class="o">,</span>
+      <span class="s">"browser"</span> <span class="o">-&gt;</span> <span class="mi">2</span>
+    <span class="o">)</span>
+
+    <span class="c1">// inject some default to cover default cases
+</span>    <span class="k">val</span> <span class="n">defaults</span> <span class="k">=</span> <span class="nc">Seq</span><span class="o">(</span>
+      <span class="k">new</span> <span class="nc">Session</span><span class="o">(</span>
+        <span class="n">landingPageId</span> <span class="k">=</span> <span class="s">""</span><span class="o">,</span>
+        <span class="n">referrerId</span> <span class="k">=</span> <span class="s">""</span><span class="o">,</span>
+        <span class="n">browser</span> <span class="k">=</span> <span class="s">""</span><span class="o">,</span>
+        <span class="n">buy</span> <span class="k">=</span> <span class="kc">false</span>
+      <span class="o">),</span>
+      <span class="k">new</span> <span class="nc">Session</span><span class="o">(</span>
+        <span class="n">landingPageId</span> <span class="k">=</span> <span class="s">""</span><span class="o">,</span>
+        <span class="n">referrerId</span> <span class="k">=</span> <span class="s">""</span><span class="o">,</span>
+        <span class="n">browser</span> <span class="k">=</span> <span class="s">""</span><span class="o">,</span>
+        <span class="n">buy</span> <span class="k">=</span> <span class="kc">true</span>
+      <span class="o">))</span>
+
+    <span class="k">val</span> <span class="n">defaultRDD</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="n">defaults</span><span class="o">)</span>
+    <span class="k">val</span> <span class="n">sessionRDD</span> <span class="k">=</span> <span class="n">td</span><span class="o">.</span><span class="n">session</span><span class="o">.</span><span class="n">union</span><span class="o">(</span><span class="n">defaultRDD</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="n">sessionRDD</span><span class="o">.</span><span class="n">map</span> <span class="o">{</span> <span class="n">session</span> <span class="k">=&gt;</span>
+      <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="o">(</span><span class="n">s</span><span class="s">"${session}"</span><span class="o">)</span>
+      <span class="k">val</span> <span class="n">label</span> <span class="k">=</span> <span class="k">if</span> <span class="o">(</span><span class="n">session</span><span class="o">.</span><span class="n">buy</span><span class="o">)</span> <span class="mf">1.0</span> <span class="k">else</span> <span class="mf">0.0</span>
+
+      <span class="k">val</span> <span class="n">feature</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">Array</span><span class="o">[</span><span class="kt">Double</span><span class="o">](</span><span class="n">featureIndex</span><span class="o">.</span><span class="n">size</span><span class="o">)</span>
+      <span class="n">feature</span><span class="o">(</span><span class="n">featureIndex</span><span class="o">(</span><span class="s">"landingPage"</span><span class="o">))</span> <span class="k">=</span>
+        <span class="n">featureCategoricalIntMap</span><span class="o">(</span><span class="s">"landingPage"</span><span class="o">)(</span><span class="n">session</span><span class="o">.</span><span class="n">landingPageId</span><span class="o">).</span><span class="n">toDouble</span>
+      <span class="n">feature</span><span class="o">(</span><span class="n">featureIndex</span><span class="o">(</span><span class="s">"referrer"</span><span class="o">))</span> <span class="k">=</span>
+        <span class="n">featureCategoricalIntMap</span><span class="o">(</span><span class="s">"referrer"</span><span class="o">)(</span><span class="n">session</span><span class="o">.</span><span class="n">referrerId</span><span class="o">).</span><span class="n">toDouble</span>
+      <span class="n">feature</span><span class="o">(</span><span class="n">featureIndex</span><span class="o">(</span><span class="s">"browser"</span><span class="o">))</span> <span class="k">=</span>
+        <span class="n">featureCategoricalIntMap</span><span class="o">(</span><span class="s">"browser"</span><span class="o">)(</span><span class="n">session</span><span class="o">.</span><span class="n">browser</span><span class="o">).</span><span class="n">toDouble</span>
+
+      <span class="nc">LabeledPoint</span><span class="o">(</span><span class="n">label</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">feature</span><span class="o">))</span>
+    <span class="o">}.</span><span class="n">cache</span><span class="o">()</span>
+
+    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="o">(</span><span class="n">s</span><span class="s">"labelelPoints count: ${labeledPoints.count()}"</span><span class="o">)</span>
+    <span class="k">new</span> <span class="nc">PreparedData</span><span class="o">(</span>
+      <span class="n">labeledPoints</span> <span class="k">=</span> <span class="n">labeledPoints</span><span class="o">,</span>
+      <span class="n">featureIndex</span> <span class="k">=</span> <span class="n">featureIndex</span><span class="o">,</span>
+      <span class="n">featureCategoricalIntMap</span> <span class="k">=</span> <span class="n">featureCategoricalIntMap</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&#39;s <code>train</code> function.</p><h2 id='algorithm' class='header-anchors'>Algorithm</h2><p>In MyLeadScoring/src/main/scala/<strong><em>ALSAlgorithm.scala</em></strong>, the two methods of the algorithm class are <code>train</code> and <code>predict</code>. <code>train</code> is responsible for training the predictive model; <code>predict</code> is responsible for using this model to make prediction.</p><p>The default algorithm is Spark&#39;s MLlib <a href="https://spark.apache.org/docs/latest/mllib-ensembles.html#random-forests">RandomForest algorithm</a>.</p><h3 id='algorithm-parameters' class='header-anchors'>Algorithm parameters</h3><p>The Algorithm takes the following parameters, as defined by the <code>AlgorithmParams</code> case class:</p><div class="highlight scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: 
 right"><pre class="lineno">1
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+8</pre></td><td class="code"><pre><span class="k">case</span> <span class="k">class</span> <span class="nc">RFAlgorithmParams</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="n">seed</span><span class="k">:</span> <span class="kt">Option</span><span class="o">[</span><span class="kt">Int</span><span class="o">]</span>
+<span class="o">)</span> <span class="k">extends</span> <span class="nc">Params</span>
+</pre></td></tr></tbody></table> </div> <p>You can find more description of the parameters in MLlib&#39;s <a href="https://spark.apache.org/docs/latest/mllib-ensembles.html#random-forests">RandomForest documentation</a> and <a href="https://spark.apache.org/docs/latest/mllib-decision-tree.html">Decision Tree documentation</a>.</p><p>The values of these parameters can be specified in <em>algorithms</em> of MyLeadScoring/<strong><em>engine.json</em></strong>:</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
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+17
+18</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">"randomforest"</span>,
+      <span class="s2">"params"</span>: <span class="o">{</span>
+        <span class="s2">"numClasses"</span>: 3,
+        <span class="s2">"numTrees"</span>: 5,
+        <span class="s2">"featureSubsetStrategy"</span>: <span class="s2">"auto"</span>,
+        <span class="s2">"impurity"</span>: <span class="s2">"variance"</span>,
+        <span class="s2">"maxDepth"</span>: 4,
+        <span class="s2">"maxBins"</span>: 100,
+        <span class="s2">"seed"</span> : 12345
+      <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 of the <code>RFAlgorithm</code> class.</p><div class="highlight scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
+2
+3
+4</pre></td><td class="code"><pre><span class="k">class</span> <span class="nc">RFAlgorithm</span><span class="o">(</span><span class="k">val</span> <span class="n">ap</span><span class="k">:</span> <span class="kt">RFAlgorithmParams</span><span class="o">)</span>
+  <span class="k">extends</span> <span class="n">P2LAlgorithm</span><span class="o">[</span><span class="kt">PreparedData</span>, <span class="kt">RFModel</span>, <span class="kt">Query</span>, <span class="kt">PredictedResult</span><span class="o">]</span> <span class="o">{</span>
+    <span class="o">...</span>
+<span class="o">}</span>
+</pre></td></tr></tbody></table> </div> <h3 id='train(...)' class='header-anchors'>train(...)</h3><p><code>train</code> is called when you run <strong>pio train</strong> to train a predictive model.</p><p>The algorithm first generates the <code>categoricalFeaturesInfo</code> which is required by the MLlib. This indicates how many categorical values for each categorical features. Then it calls <code>RandomForest.trainRegressor()</code> to train a <code>RandomForestModel</code> to predict the probability that the user may convert.</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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+30</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">pd</span><span class="k">:</span> <span class="kt">PreparedData</span><span class="o">)</span><span class="k">:</span> <span class="kt">RFModel</span> <span class="o">=</span> <span class="o">{</span>
+
+    <span class="k">val</span> <span class="n">categoricalFeaturesInfo</span> <span class="k">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">featureCategoricalIntMap</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">f</span><span class="o">,</span> <span class="n">m</span><span class="o">)</span> <span class="k">=&gt;</span>
+        <span class="o">(</span><span class="n">pd</span><span class="o">.</span><span class="n">featureIndex</span><span class="o">(</span><span class="n">f</span><span class="o">),</span> <span class="n">m</span><span class="o">.</span><span class="n">size</span><span class="o">)</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">"categoricalFeaturesInfo: ${categoricalFeaturesInfo}"</span><span class="o">)</span>
+
+    <span class="c1">// use random seed if seed is not specified
+</span>    <span class="k">val</span> <span class="n">seed</span> <span class="k">=</span> <span class="n">ap</span><span class="o">.</span><span class="n">seed</span><span class="o">.</span><span class="n">getOrElse</span><span class="o">(</span><span class="n">scala</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="nc">Random</span><span class="o">.</span><span class="n">nextInt</span><span class="o">())</span>
+
+    <span class="k">val</span> <span class="n">forestModel</span><span class="k">:</span> <span class="kt">RandomForestModel</span> <span class="o">=</span> <span class="nc">RandomForest</span><span class="o">.</span><span class="n">trainRegressor</span><span class="o">(</span>
+      <span class="n">input</span> <span class="k">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">labeledPoints</span><span class="o">,</span>
+      <span class="n">categoricalFeaturesInfo</span> <span class="k">=</span> <span class="n">categoricalFeaturesInfo</span><span class="o">,</span>
+      <span class="n">numTrees</span> <span class="k">=</span> <span class="n">ap</span><span class="o">.</span><span class="n">numTrees</span><span class="o">,</span>
+      <span class="n">featureSubsetStrategy</span> <span class="k">=</span> <span class="n">ap</span><span class="o">.</span><span class="n">featureSubsetStrategy</span><span class="o">,</span>
+      <span class="n">impurity</span> <span class="k">=</span> <span class="n">ap</span><span class="o">.</span><span class="n">impurity</span><span class="o">,</span>
+      <span class="n">maxDepth</span> <span class="k">=</span> <span class="n">ap</span><span class="o">.</span><span class="n">maxDepth</span><span class="o">,</span>
+      <span class="n">maxBins</span> <span class="k">=</span> <span class="n">ap</span><span class="o">.</span><span class="n">maxBins</span><span class="o">,</span>
+      <span class="n">seed</span> <span class="k">=</span> <span class="n">seed</span><span class="o">)</span>
+
+    <span class="k">new</span> <span class="nc">RFModel</span><span class="o">(</span>
+      <span class="n">forest</span> <span class="k">=</span> <span class="n">forestModel</span><span class="o">,</span>
+      <span class="n">featureIndex</span> <span class="k">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">featureIndex</span><span class="o">,</span>
+      <span class="n">featureCategoricalIntMap</span> <span class="k">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">featureCategoricalIntMap</span>
+    <span class="o">)</span>
+  <span class="o">}</span>
+
+</pre></td></tr></tbody></table> </div> <p>PredictionIO will automatically store the returned model after training.</p><p>The <code>RFModel</code> stores the <code>RandomForestModel</code>, and the <code>featureIndex</code> and <code>featureCategoricalIntMap</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</pre></td><td class="code"><pre><span class="k">class</span> <span class="nc">RFModel</span><span class="o">(</span>
+  <span class="k">val</span> <span class="n">forest</span><span class="k">:</span> <span class="kt">RandomForestModel</span><span class="o">,</span>
+  <span class="k">val</span> <span class="n">featureIndex</span><span class="k">:</span> <span class="kt">Map</span><span class="o">[</span><span class="kt">String</span>, <span class="kt">Int</span><span class="o">],</span>
+  <span class="k">val</span> <span class="n">featureCategoricalIntMap</span><span class="k">:</span> <span class="kt">Map</span><span class="o">[</span><span class="kt">String</span>, <span class="kt">Map</span><span class="o">[</span><span class="kt">String</span>, <span class="kt">Int</span><span class="o">]]</span>
+<span class="o">)</span> <span class="k">extends</span> <span class="nc">Serializable</span> <span class="o">{</span>
+  <span class="o">...</span>
+<span class="o">}</span>
+</pre></td></tr></tbody></table> </div> <h3 id='predict(...)' class='header-anchors'>predict(...)</h3><p><code>predict</code> is called when you send a JSON query to <a href="http://localhost:8000/queries.json">http://localhost:8000/queries.json</a>. PredictionIO converts the query, such as &#39;{ &quot;landingPageId&quot; : &quot;example.com/page9&quot;, &quot;referrerId&quot; : &quot;referrer10.com&quot;, &quot;browser&quot;: &quot;Firefox&quot; }&#39; to the <code>Query</code> class you defined previously in <code>Engine.scala</code>.</p><p>The <code>predict()</code> function does the following:</p> <ol> <li>convert the Query to the required feature vector input</li> <li>use the <code>RandomForestModel</code> to predict the probabilty of conversion given this feature.</li> </ol> <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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+49</pre></td><td class="code"><pre>
+  <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">RFModel</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">featureIndex</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">featureIndex</span>
+    <span class="k">val</span> <span class="n">featureCategoricalIntMap</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">featureCategoricalIntMap</span>
+
+    <span class="k">val</span> <span class="n">landingPageId</span> <span class="k">=</span> <span class="n">query</span><span class="o">.</span><span class="n">landingPageId</span>
+    <span class="k">val</span> <span class="n">referrerId</span> <span class="k">=</span> <span class="n">query</span><span class="o">.</span><span class="n">referrerId</span>
+    <span class="k">val</span> <span class="n">browser</span> <span class="k">=</span> <span class="n">query</span><span class="o">.</span><span class="n">browser</span>
+
+    <span class="c1">// look up categorical feature Int for landingPageId
+</span>    <span class="k">val</span> <span class="n">landingFeature</span> <span class="k">=</span> <span class="n">lookupCategoricalInt</span><span class="o">(</span>
+      <span class="n">featureCategoricalIntMap</span> <span class="k">=</span> <span class="n">featureCategoricalIntMap</span><span class="o">,</span>
+      <span class="n">feature</span> <span class="k">=</span> <span class="s">"landingPage"</span><span class="o">,</span>
+      <span class="n">value</span> <span class="k">=</span> <span class="n">landingPageId</span><span class="o">,</span>
+      <span class="n">default</span> <span class="k">=</span> <span class="s">""</span>
+    <span class="o">).</span><span class="n">toDouble</span>
+
+
+    <span class="c1">// look up categorical feature Int for referrerId
+</span>    <span class="k">val</span> <span class="n">referrerFeature</span> <span class="k">=</span> <span class="n">lookupCategoricalInt</span><span class="o">(</span>
+      <span class="n">featureCategoricalIntMap</span> <span class="k">=</span> <span class="n">featureCategoricalIntMap</span><span class="o">,</span>
+      <span class="n">feature</span> <span class="k">=</span> <span class="s">"referrer"</span><span class="o">,</span>
+      <span class="n">value</span> <span class="k">=</span> <span class="n">referrerId</span><span class="o">,</span>
+      <span class="n">default</span> <span class="k">=</span> <span class="s">""</span>
+    <span class="o">).</span><span class="n">toDouble</span>
+
+    <span class="c1">// look up categorical feature Int for brwoser
+</span>    <span class="k">val</span> <span class="n">browserFeature</span> <span class="k">=</span> <span class="n">lookupCategoricalInt</span><span class="o">(</span>
+      <span class="n">featureCategoricalIntMap</span> <span class="k">=</span> <span class="n">featureCategoricalIntMap</span><span class="o">,</span>
+      <span class="n">feature</span> <span class="k">=</span> <span class="s">"browser"</span><span class="o">,</span>
+      <span class="n">value</span> <span class="k">=</span> <span class="n">browser</span><span class="o">,</span>
+      <span class="n">default</span> <span class="k">=</span> <span class="s">""</span>
+    <span class="o">).</span><span class="n">toDouble</span>
+
+    <span class="c1">// create feature Array
+</span>    <span class="k">val</span> <span class="n">feature</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">Array</span><span class="o">[</span><span class="kt">Double</span><span class="o">](</span><span class="n">model</span><span class="o">.</span><span class="n">featureIndex</span><span class="o">.</span><span class="n">size</span><span class="o">)</span>
+    <span class="n">feature</span><span class="o">(</span><span class="n">featureIndex</span><span class="o">(</span><span class="s">"landingPage"</span><span class="o">))</span> <span class="k">=</span> <span class="n">landingFeature</span>
+    <span class="n">feature</span><span class="o">(</span><span class="n">featureIndex</span><span class="o">(</span><span class="s">"referrer"</span><span class="o">))</span> <span class="k">=</span> <span class="n">referrerFeature</span>
+    <span class="n">feature</span><span class="o">(</span><span class="n">featureIndex</span><span class="o">(</span><span class="s">"browser"</span><span class="o">))</span> <span class="k">=</span> <span class="n">browserFeature</span>
+
+    <span class="k">val</span> <span class="n">score</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forest</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">feature</span><span class="o">))</span>
+    <span class="k">new</span> <span class="nc">PredictedResult</span><span class="o">(</span><span class="n">score</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>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 MyLeadScoring/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
+10</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="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">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><div class="alert-message note"><p>An engine can train multiple models if you specify more than one Algorithm component in <code>object LeadScoringEngine</code> inside <strong><em>Engine.scala</em></strong> and the corresponding parameters in <strong><em>engine.json</em></strong>. Since only one algorithm is implemented by default, this <code>Seq</code> contains one element.</p></div></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.predict
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