http://git-wip-us.apache.org/repos/asf/incubator-predictionio-site/blob/dd18a676/templates/classification/reading-custom-properties/index.html ---------------------------------------------------------------------- diff --git a/templates/classification/reading-custom-properties/index.html b/templates/classification/reading-custom-properties/index.html new file mode 100644 index 0000000..1551f72 --- /dev/null +++ b/templates/classification/reading-custom-properties/index.html @@ -0,0 +1,68 @@ +<!DOCTYPE html><html><head><title>Reading Custom Properties (Classification)</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="Reading Custom Properties (Classification)"/><link rel="canonical" href="https://predictionio.incubator.apache.org/templates/classification/reading-custom-properties/"/><link 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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">Read Custom Properties</span></li></ul></div><div id="page-title"><h1>Reading Custom Properties (Classification)</h1></div></div><div class="content"> <p>By default, the classification template reads 4 properties of a user entity: "attr0", "attr1", "attr2" and "plan". You can modify the <a href="/templates/classification/dase/#data">default DataSource</a> to read your custom properties or different En tity Type.</p><p>In this example, we modify DataSource to read properties "featureA", "featureB", "featureC", "featureD" and "label" for entity type "item". You can find the complete modified source code <a href="https://github.com/apache/incubator-predictionio/tree/develop/examples/scala-parallel-classification/reading-custom-properties";>here</a>.</p> <blockquote> <blockquote> <p>Note: you also need import events with these properties accordingly.</p></blockquote> </blockquote> <p>Modify the <code>readTraining()</code> and <code>readEval()</code> in DataSource.scala:</p> <ul> <li>modify the <code>entityType</code> parameter</li> <li>modify the list of properties names in the <code>required</code> parameter</li> <li>modify how to create the <code>LabeledPoint</code> object using the entity properties</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</pre></td><td class="code"><pre> <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="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">"item"</span><span class="o">,</span> <span class="c1">// MODIFIED +</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="c1">// MODIFIED +</span> <span class="s">"featureA"</span><span class="o">,</span> <span class="s">"featureB"</span><span class="o">,</span> <span class="s">"featureC"</span><span class="o">,</span> <span class="s">"featureD"</span><span class="o">,</span> <span class="s">"label"</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="c1">// MODIFIED +</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">"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="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">"featureA"</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">"featureB"</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">"featureC"</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">"featureD"</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: ${e}."</span><span class="o">)</span> + <span class="k">throw</span> <span class="n">e</span> + <span class="o">}</span> + <span class="o">}</span> + <span class="o">}.</span><span class="n">cache</span><span class="o">()</span> + <span class="o">...</span> + <span class="o">}</span> +</pre></td></tr></tbody></table> </div> <p>Lastly, redefine the Query class parameters to take in four double values: featureA, featureB, featureC, and featureD. Now, to send a query, the field names must be changed accordingly:</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td class="code"><pre><span class="gp">$ </span>curl -H <span class="s2">"Content-Type: application/json"</span> -d <span class="s1">'{ "featureA":2, "featureB":0, "featureC":0, "featureD":0 }'</span> http://localhost:8000/queries.json +</pre></td></tr></tbody></table> </div> <p>That's it! 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http://git-wip-us.apache.org/repos/asf/incubator-predictionio-site/blob/dd18a676/templates/classification/reading-custom-properties/index.html.gz ---------------------------------------------------------------------- diff --git a/templates/classification/reading-custom-properties/index.html.gz b/templates/classification/reading-custom-properties/index.html.gz new file mode 100644 index 0000000..2be724a Binary files /dev/null and b/templates/classification/reading-custom-properties/index.html.gz differ http://git-wip-us.apache.org/repos/asf/incubator-predictionio-site/blob/dd18a676/templates/complementarypurchase/dase/index.html ---------------------------------------------------------------------- diff --git a/templates/complementarypurchase/dase/index.html b/templates/complementarypurchase/dase/index.html new file mode 100644 index 0000000..14008fe --- /dev/null +++ b/templates/complementarypurchase/dase/index.html @@ -0,0 +1,416 @@ +<!DOCTYPE html><html><head><title>DASE Components 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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 (Complementary Purchase)</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 c lass="final" href="/batchpredict/"><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></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 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></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</span></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="/eval uation/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></a></li><li class="level-3"><a class="final" href="/templates/recommendation/batch-evaluator/"><span>Batch Persistable Evaluator</span></a></li></ul></li><li class="level-2"><a cl ass="expandible" href="#"><span>E-Commerce Recommendation</span></a><ul><li class="level-3"><a class="final" href="/templates/ecommercerecommendation/quickstart/"><span>Quick Start</span></a></li><li class="level-3"><a class="final" href="/templates/ecommercerecommendation/dase/"><span>DASE</span></a></li><li class="level-3"><a class="final" href="/templates/ecommercerecommendation/how-to/"><span>How-To</span></a></li><li class="level-3"><a class="final" href="/templates/ecommercerecommendation/train-with-rate-event/"><span>Train with Rate Event</span></a></li><li class="level-3"><a class="final" href="/templates/ecommercerecommendation/adjust-score/"><span>Adjust Score</span></a></li></ul></li><li class="level-2"><a class="expandible" href="#"><span>Similar Product</span></a><ul><li class="level-3"><a class="final" href="/templates/similarproduct/quickstart/"><span>Quick Start</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/dase/"><span>DASE</spa n></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" 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 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/taps ter/"><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 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="/cli/"><span>Command-line Interface</span></a></li><li class="level-2"><a class="final" href="/resources/release/"><span>Release Cadence</span></a></li><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><li class="level-1"><a class="expandible" href="#"><span>Apache Software Foundation</span></a><ul><li class="level-2"><a class="final" href="http s://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="page-title"><h1>DASE Components Explained (Complementary Purchase)</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/complementarypurchase/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 (Complementary Purchase)</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 Complementary > Purchase Engine Template.</p><div class="alert-message note"><p>Evaluator > will not be covered in thi s 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>MyComplementaryPurchase</em> takes a JSON prediction query, e.g. <code>{ "items" : ["s2i1"], "num" : 3 }</code>, and return a JSON predicted result. In MyComplementaryPurchase/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</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">items</span><span class="k">:</span> <span class="kt">Set</span><span class="o">[</span><span class="kt">String</span><span class="o">],</span> <span class="n">num</span><span class="k">:</span> <span class="kt">Int</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 +2 +3 +4 +5 +6 +7 +8 +9 +10 +11 +12 +13 +14 +15 +16 +17 +18 +19 +20 +21</pre></td><td class="code"><pre><span class="p">{</span><span class="w"> + </span><span class="s2">"rules"</span><span class="p">:[</span><span class="w"> + </span><span class="p">{</span><span class="w"> + </span><span class="s2">"cond"</span><span class="p">:[</span><span class="s2">"s2i1"</span><span class="p">],</span><span class="w"> + </span><span class="s2">"itemScores"</span><span class="p">:[</span><span class="w"> + </span><span class="p">{</span><span class="w"> + </span><span class="s2">"item"</span><span class="p">:</span><span class="s2">"s2i2"</span><span class="p">,</span><span class="w"> + </span><span class="s2">"support"</span><span class="p">:</span><span class="mf">0.2</span><span class="p">,</span><span class="w"> + </span><span class="s2">"confidence"</span><span class="p">:</span><span class="mf">0.9090909090909091</span><span class="p">,</span><span class="w"> + </span><span class="s2">"lift"</span><span class="p">:</span><span class="mf">3.787878787878788</span><span class="w"> + </span><span class="p">},</span><span class="w"> + </span><span class="p">{</span><span class="w"> + </span><span class="s2">"item"</span><span class="p">:</span><span class="s2">"s2i3"</span><span class="p">,</span><span class="w"> + </span><span class="s2">"support"</span><span class="p">:</span><span class="mf">0.14</span><span class="p">,</span><span class="w"> + </span><span class="s2">"confidence"</span><span class="p">:</span><span class="mf">0.6363636363636364</span><span class="p">,</span><span class="w"> + </span><span class="s2">"lift"</span><span class="p">:</span><span class="mf">3.535353535353535</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="p">]</span><span class="w"> +</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 +4 +5 +6 +7 +8 +9</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">rules</span><span class="k">:</span> <span class="kt">Array</span><span class="o">[</span><span class="kt">Rule</span><span class="o">])</span> + <span class="k">extends</span> <span class="nc">Serializable</span> + +<span class="k">case</span> <span class="k">class</span> <span class="nc">Rule</span><span class="o">(</span><span class="n">cond</span><span class="k">:</span> <span class="kt">Set</span><span class="o">[</span><span class="kt">String</span><span class="o">],</span> <span class="n">itemScores</span><span class="k">:</span> <span class="kt">Array</span><span class="o">[</span><span class="kt">ItemScore</span><span class="o">])</span> + <span class="k">extends</span> <span class="nc">Serializable</span> + +<span class="k">case</span> <span class="k">class</span> <span class="nc">ItemScore</span><span class="o">(</span> + <span class="n">item</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span> <span class="n">support</span><span class="k">:</span> <span class="kt">Double</span><span class="o">,</span> <span class="n">confidence</span><span class="k">:</span> <span class="kt">Double</span><span class="o">,</span> <span class="n">lift</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>ComplementaryPurchaseEngine</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">ComplementaryPurchaseEngine</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">"algo"</span> <span class="o">-></span> <span class="n">classOf</span><span class="o">[</span><span class="kt">Algorithm</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>ComplementaryPurchaseEngine</code> will be explained below.</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 MyComplementaryPurchase/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</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="c1">// get all "user" "buy" "item" events +</span> <span class="k">val</span> <span class="n">buyEvents</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">BuyEvent</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">List</span><span class="o">(</span><span class="s">"buy"</span><span class="o">)),</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="o">.</span><span class="n">map</span> <span class="o">{</span> <span class="n">event</span> <span class="k">=></span> + <span class="k">try</span> <span class="o">{</span> + <span class="k">new</span> <span class="nc">BuyEvent</span><span class="o">(</span> + <span class="n">user</span> <span class="k">=</span> <span class="n">event</span><span class="o">.</span><span class="n">entityId</span><span class="o">,</span> + <span class="n">item</span> <span class="k">=</span> <span class="n">event</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">t</span> <span class="k">=</span> <span class="n">event</span><span class="o">.</span><span class="n">eventTime</span><span class="o">.</span><span class="n">getMillis</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">"Cannot convert ${event} to BuyEvent. ${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">buyEvents</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 MyComplementaryPurchase/<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 Complementary Purchase Engine Template requires "buy" events.</p><p><code>PEventStore.find(...)</code> specifies the events that you want to read. In this case, "user buy item" events are read and then each is mapped to a <code>BuyEvent</code> object.</p><p><code>BuyEvent</code> case class 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</pre></td><td class="code"><pre><span class="k">case</span> <span class="k">class</span> <span class="nc">BuyEvent</span><span class="o">(</span><span class="n">user</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span> <span class="n">item</span><span class="k">:</span> <span clas s="kt">String</span><span class="o">,</span> <span class="n">t</span><span class="k">:</span> <span class="kt">Long</span><span class="o">)</span> +</pre></td></tr></tbody></table> </div> <p><code>TrainingData</code> contains an RDD of <code>BuyEvent</code> objects. 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">buyEvents</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">BuyEvent</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> <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>.</p></div><h3 id='data-preparator' class='header-anchors'>Data Preparator</h3><p>In MyComplementaryPurchase/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>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 +12 +13</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="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">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="k">new</span> <span class="nc">PreparedData</span><span class="o">(</span><span class="n">buyEvents</span> <span class="k">=</span> <span class="n">td</span><span class="o">.</span><span class="n">buyEvents</span><span class="o">)</span> + <span class="o">}</span> +<span class="o">}</span> + +<span class="k">class</span> <span class="nc">PreparedData</span><span class="o">(</span> + <span class="k">val</span> <span class="n">buyEvents</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">BuyEvent</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 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 MyComplementaryPurchase/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 based on concept of <a href="http://en.wikipedia.org/wiki/Association_rule_learning";>Association Rule Learning</a> to find interesting association rules (A implies B) that indicates additional item (B) may be bought together given a list of items (A). A is the <em>condition</em> and B is the <em>consequence</em>.</p><h3 id='algorithm-parameters' class='header-anchors'>Algorithm parameters</h3><p>The Algorithm takes the fo llowing 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 +2 +3 +4 +5 +6 +7 +8 +9 +10</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">basketWindow</span><span class="k">:</span> <span class="kt">Int</span><span class="o">,</span> <span class="c1">// in seconds +</span> <span class="n">maxRuleLength</span><span class="k">:</span> <span class="kt">Int</span><span class="o">,</span> + <span class="n">minSupport</span><span class="k">:</span> <span class="kt">Double</span><span class="o">,</span> + <span class="n">minConfidence</span><span class="k">:</span> <span class="kt">Double</span><span class="o">,</span> + <span class="n">minLift</span><span class="k">:</span> <span class="kt">Double</span><span class="o">,</span> + <span class="n">minBasketSize</span><span class="k">:</span> <span class="kt">Int</span><span class="o">,</span> + <span class="n">maxNumRulesPerCond</span><span class="k">:</span> <span class="kt">Int</span> <span class="c1">// max number of rules per condition +</span> <span class="o">)</span> <span class="k">extends</span> <span class="nc">Params</span> + +</pre></td></tr></tbody></table> </div> <p>Parameter description:</p> <ul> <li><strong>basketWindow</strong>: The buy event is considered as the same basket as previous one if the time difference is within this window (in unit of seconds). For example, if it's set to 120, it means that if the user buys item B within 2 minutes of previous purchase (item A), then the item set [A, B] is considered as the same basket. The purchase of this <em>basket</em> is referred as one <em>transaction</em>.</li> <li><strong>maxRuleLength</strong>: The maximum length of the association rule length. Must be at least 2. For example, rule of "A implies B" has length of 2 while rule "A, B implies C" has a length of 3. Increasing this number will incrase the training time significantly because more combinations are considered.</li> <li><strong>minSupport</strong>: The minimum required <em>support</em> for the item set to be considered as rule (valid range is 0 to 1). It's the p ercentage of the item set appearing among all transcations. This is used to filter out infrequent item set. For example, setting to 0.1 means that the item set must appear in 10 % of all transactions.</li> <li><strong>minConfidence</strong>: The minimum <em>confidence</em> required for the rules (valid range is 0 to 1). The confidence indicates the probability of the condition and conseuquence appear in the same transaction. For example, if A appears in 30 transactions and the item set [A, B] appears in 20 transactions, then the rule "A implies B" has confidence of 0.66.</li> <li><strong>minLift</strong>: The minimum <em>lift</em> required for the rule. It should be set to 1 to find high quality rule. It's the confidence of the rule divided by the support of the consequence. It is used to filter out rules that the consequence is very frequent anyway regardless of the condition.</li> <li><strong>minBasketSize</strong>: The minimum number of items in basket to be conside red by algorithm. This value must be at least 2.</li> <li><strong>maxNumRulesPerCond</strong>: Maximum number of rules generated per condition and stored in the model. By default, the top rules are sorted by <em>lift</em> score.</li> </ul> <div class="alert-message info"><p>If you import your own data and the engine doesn't return any results, it could be caused by the following reasons: (1) the algorithm parameter constraint is too high and the algo couldn't find rules that satisfy the condition. you could try setting the following param to 0: <strong>minSupport</strong>, <strong>minConfidence</strong>, <strong>minLift</strong> and then see if anything returned (regardless of recommendation quality), and then adjust the parameter accordingly. (2) the complementary purchase engine requires buy event with correct eventTime. If you import data without specifying eventTime, the SDK will use current time because it assumes the event happens in real time (which is not the case if you import as batch offline), resulting in that all buy events are treated as one big transcation while they should be treated as multiple transcations.</p></div><p>The values of these parameters can be specified in <em>algorithms</em> of MyComplementaryPurchase/<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 +13 +14 +15 +16 +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">"algo"</span>, + <span class="s2">"params"</span>: <span class="o">{</span> + <span class="s2">"basketWindow"</span> : 120, + <span class="s2">"maxRuleLength"</span> : 2, + <span class="s2">"minSupport"</span>: 0.1, + <span class="s2">"minConfidence"</span>: 0.6, + <span class="s2">"minLift"</span> : 1.0, + <span class="s2">"minBasketSize"</span> : 2, + <span class="s2">"maxNumRulesPerCond"</span>: 5 + <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>Algorithm</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">Algorithm</span><span class="o">(</span><span class="k">val</span> <span class="n">ap</span><span class="k">:</span> <span class="kt">AlgorithmParams</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">Model</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. The algorithm first find all basket transcations, generates and filters the association rules based on the algorithm parameters:</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">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">Model</span> <span class="o">=</span> <span class="o">{</span> + <span class="k">val</span> <span class="n">windowMillis</span> <span class="k">=</span> <span class="n">ap</span><span class="o">.</span><span class="n">basketWindow</span> <span class="o">*</span> <span class="mi">1000</span> + + <span class="o">...</span> + + <span class="k">val</span> <span class="n">transactions</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">Set</span><span class="o">[</span><span class="kt">String</span><span class="o">]]</span> <span class="k">=</span> <span class="o">...</span> + + <span class="k">val</span> <span class="n">totalTransaction</span> <span class="k">=</span> <span class="n">transactions</span><span class="o">.</span><span class="n">count</span><span class="o">()</span> + <span class="k">val</span> <span class="n">minSupportCount</span> <span class="k">=</span> <span class="n">ap</span><span class="o">.</span><span class="n">minSupport</span> <span class="o">*</span> <span class="n">totalTransaction</span> + + <span class="o">...</span> + + <span class="c1">// generate item sets +</span> <span class="k">val</span> <span class="n">itemSets</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">Set</span><span class="o">[</span><span class="kt">String</span><span class="o">]]</span> <span class="k">=</span> <span class="n">transactions</span> + <span class="o">.</span><span class="n">flatMap</span> <span class="o">{</span> <span class="n">tran</span> <span class="k">=></span> + <span class="o">(</span><span class="mi">1</span> <span class="n">to</span> <span class="n">ap</span><span class="o">.</span><span class="n">maxRuleLength</span><span class="o">).</span><span class="n">flatMap</span><span class="o">(</span><span class="n">n</span> <span class="k">=></span> <span class="n">tran</span><span class="o">.</span><span class="n">subsets</span><span class="o">(</span><span class="n">n</span><span class="o">))</span> + <span class="o">}</span> + + <span class="o">...</span> + + <span class="k">val</span> <span class="n">itemSetCount</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[(</span><span class="kt">Set</span><span class="o">[</span><span class="kt">String</span><span class="o">]</span>, <span class="kt">Int</span><span class="o">)]</span> <span class="k">=</span> <span class="o">...</span> + + <span class="o">...</span> + + <span class="k">val</span> <span class="n">rules</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[(</span><span class="kt">Set</span><span class="o">[</span><span class="kt">String</span><span class="o">]</span>, <span class="kt">RuleScore</span><span class="o">)]</span> <span class="k">=</span> <span class="o">...</span> + + <span class="k">val</span> <span class="n">sortedRules</span> <span class="k">=</span> <span class="n">rules</span><span class="o">.</span><span class="n">groupByKey</span> + <span class="o">.</span><span class="n">mapValues</span><span class="o">(</span><span class="n">iter</span> <span class="k">=></span> + <span class="n">iter</span><span class="o">.</span><span class="n">toVector</span> + <span class="o">.</span><span class="n">sortBy</span><span class="o">(</span><span class="k">_</span><span class="o">.</span><span class="n">lift</span><span class="o">)(</span><span class="nc">Ordering</span><span class="o">.</span><span class="nc">Double</span><span class="o">.</span><span class="n">reverse</span><span class="o">)</span> + <span class="o">.</span><span class="n">take</span><span class="o">(</span><span class="n">ap</span><span class="o">.</span><span class="n">maxNumRulesPerCond</span><span class="o">)</span> + <span class="o">)</span> + <span class="o">.</span><span class="n">collectAsMap</span><span class="o">.</span><span class="n">toMap</span> + + <span class="k">new</span> <span class="nc">Model</span><span class="o">(</span><span class="n">sortedRules</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, i.e. the <code>Model</code> object.</p><p>The <code>Model</code> stores the top rules for each condition:</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">Model</span><span class="o">(</span> + <span class="k">val</span> <span class="n">rules</span><span class="k">:</span> <span class="kt">Map</span><span class="o">[</span><span class="kt">Set</span><span class="o">[</span><span class="kt">String</span><span class="o">]</span>, <span class="kt">Vector</span><span class="o">[</span><span class="kt">RuleScore</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 <code>{ "items" : ["s2i1"], "num" : 3 }</code> 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>find all possible subset of the items in query</li> <li>use the subsets as condition to look up the model and return the rules for each condition.</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 +2 +3 +4 +5 +6 +7 +8 +9 +10 +11 +12 +13 +14 +15 +16 +17 +18 +19 +20 +21 +22 +23 +24 +25</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">Model</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">conds</span> <span class="k">=</span> <span class="o">(</span><span class="mi">1</span> <span class="n">to</span> <span class="n">maxCondLength</span><span class="o">).</span><span class="n">flatMap</span><span class="o">(</span><span class="n">n</span> <span class="k">=></span> <span class="n">query</span><span class="o">.</span><span class="n">items</span><span class="o">.</span><span class="n">subsets</span><span class="o">(</span><span class="n">n</span><span class="o">))</span> + + <span class="k">val</span> <span class="n">rules</span> <span class="k">=</span> <span class="n">conds</span><span class="o">.</span><span class="n">map</span> <span class="o">{</span> <span class="n">cond</span> <span class="k">=></span> + <span class="n">model</span><span class="o">.</span><span class="n">rules</span><span class="o">.</span><span class="n">get</span><span class="o">(</span><span class="n">cond</span><span class="o">).</span><span class="n">map</span><span class="o">{</span> <span class="n">vec</span> <span class="k">=></span> + <span class="k">val</span> <span class="n">itemScores</span> <span class="k">=</span> <span class="n">vec</span><span class="o">.</span><span class="n">take</span><span class="o">(</span><span class="n">query</span><span class="o">.</span><span class="n">num</span><span class="o">).</span><span class="n">map</span> <span class="o">{</span> <span class="n">rs</span> <span class="k">=></span> + <span class="k">new</span> <span class="nc">ItemScore</span><span class="o">(</span> + <span class="n">item</span> <span class="k">=</span> <span class="n">rs</span><span class="o">.</span><span class="n">conseq</span><span class="o">,</span> + <span class="n">support</span> <span class="k">=</span> <span class="n">rs</span><span class="o">.</span><span class="n">support</span><span class="o">,</span> + <span class="n">confidence</span> <span class="k">=</span> <span class="n">rs</span><span class="o">.</span><span class="n">confidence</span><span class="o">,</span> + <span class="n">lift</span> <span class="k">=</span> <span class="n">rs</span><span class="o">.</span><span class="n">lift</span> + <span class="o">)</span> + <span class="o">}.</span><span class="n">toArray</span> + <span class="nc">Rule</span><span class="o">(</span><span class="n">cond</span> <span class="k">=</span> <span class="n">cond</span><span class="o">,</span> <span class="n">itemScores</span> <span class="k">=</span> <span class="n">itemScores</span><span class="o">)</span> + <span class="o">}</span> + <span class="o">}.</span><span class="n">flatten</span><span class="o">.</span><span class="n">toArray</span> + + <span class="k">new</span> <span class="nc">PredictedResult</span><span class="o">(</span><span class="n">rules</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 MyComplementaryPurchase/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 +11</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 ComplementaryPurchaseEngine</code> inside <strong><em>Engine.scala</em></strong> and the corresponding parameters in <strong><em>engine.json</em></strong>. Since only one <code>Algorithm</code> 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 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></l i><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.apache.org/community/contribute-code/" target="blank">Contribute</a></li><li><a href="//github.com/apache/incubator-predictionio" target="blank">Source Code</a></li><li><a href="//issues.apache.org/jira/browse/PIO" target="blank">Bug Tracker</a></li><li><a href="mailto:[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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