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class="level-3"><a class="final" 
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href="/templates/recommendation/customize-data-prep/"><span>Customize Data 
Preparator</span></a></li><li class="level-3"><a class="final" 
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Serving</span></a></li><li class="level-3"><a class="final" 
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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/reco
 mmendation/batch-evaluator/"><span>Batch Persistable 
Evaluator</span></a></li></ul></li><li class="level-2"><a class="expandible" 
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class="final" href="/templates/ecommercerecommendation/quickstart/"><span>Quick 
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with Rate Event</span></a></li><li class="level-3"><a class="final" 
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Users</span></a></li></ul></li><li class="level-2"><a class="expandible" 
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 class="col-md-9 col-sm-12"><div class="content-header hidden-md 
hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a 
href="#">Customizing an Engine</a><span 
class="spacer">&gt;</span></li><li><span class="last">Implement 
DASE</span></li></ul></div><div id="page-title"><h1>Implementing 
DASE</h1></div></div><div id="
 table-of-content-wrapper"><h5>On this page</h5><aside 
id="table-of-contents"><ul> <li> <a href="#datasource">DataSource</a> <ul> <li> 
<a href="#readtraining">readTraining()</a> </li> <li> <a 
href="#using-peventstore-engine-api">Using PEventStore Engine API</a> </li> 
</ul> </li> <li> <a href="#preparator">Preparator</a> <ul> <li> <a 
href="#prepare">prepare()</a> </li> </ul> </li> <li> <a 
href="#algorithm">Algorithm</a> <ul> <li> <a href="#train">train()</a> </li> 
<li> <a href="#predict">predict()</a> </li> <li> <a 
href="#p2lalgorithm">P2LAlgorithm</a> </li> <li> <a 
href="#palgorithm">PAlgorithm</a> </li> <li> <a 
href="#using-leventstore-engine-api-in-predict">using LEventStore Engine API in 
predict()</a> </li> </ul> </li> <li> <a href="#serving">Serving</a> <ul> <li> 
<a href="#serve">serve()</a> </li> </ul> </li> </ul> </aside><hr/><a 
id="edit-page-link" 
href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/customize/dase.html.md.erb";><img
 src="/image
 s/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="#">Customizing an Engine</a><span 
class="spacer">&gt;</span></li><li><span class="last">Implement 
DASE</span></li></ul></div><div id="page-title"><h1>Implementing 
DASE</h1></div></div><div class="content"> <p>This section gives you an 
overview of DASE components and how to implement them. You will find links to 
some engine templates for more concrete examples.</p><h1 id='datasource' 
class='header-anchors'>DataSource</h1><p>DataSource reads and selects useful 
data from the Event Store (data store of the Event Server) and returns 
TrainingData.</p><h2 id='readtraining()' 
class='header-anchors'>readTraining()</h2><p>You need to implment 
readTraining() of <a 
href="https://predictionio.incubator.apache.org/api/current/#org.apache.predictionio.controller.PDataSource";>PDataSource</a>,
 where you can use the <a href="http
 
s://predictionio.incubator.apache.org/api/current/#org.apache.predictionio.data.store.PEventStore$">PEventStore
 Engine API</a> to read the events and create the TrainingData based on the 
events.</p><p>The following code example reads user &quot;view&quot; and 
&quot;buy&quot; item events, filters specific type of events for future 
processing and returns TrainingData accordingly.</p><div class="highlight 
scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" 
style="text-align: right"><pre class="lineno">1
+<!DOCTYPE html><html><head><title>Implementing DASE</title><meta 
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 v id="nav-main"><ul><li class="level-1"><a class="expandible" 
href="/"><span>Apache PredictionIO™ Documentation</span></a><ul><li 
class="level-2"><a class="final" href="/"><span>Welcome to Apache 
PredictionIO</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</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="/app
 integration/"><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="/batchpredict/"><span>Batch Predictions</span></a></li><li 
class="level-2"><a class="final" href="/deploy/monitoring/"><span>Monitoring En
 gine</span></a></li><li class="level-2"><a class="final" 
href="/deploy/engineparams/"><span>Setting Engine Parameters</span></a></li><li 
class="level-2"><a class="final" href="/deploy/enginevariants/"><span>Deploying 
Multiple Engine Variants</span></a></li><li class="level-2"><a class="final" 
href="/deploy/plugin/"><span>Engine Server Plugin</span></a></li></ul></li><li 
class="level-1"><a class="expandible" href="#"><span>Customizing an 
Engine</span></a><ul><li class="level-2"><a class="final" 
href="/customize/"><span>Learning DASE</span></a></li><li class="level-2"><a 
class="final active" 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><li class="level-2"><a class="final" 
href="/datacollection/plugin/"><span>Event Server 
Plugin</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><span>Choosing an Algorith
 m(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</s
 pan></a></li><li class="level-2"><a class="final" 
href="/evaluation/metricbuild/"><span>Building Evaluation 
Metrics</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><span>System Architecture</span></a><ul><li class="level-2"><a 
class="final" href="/system/"><span>Architecture Overview</span></a></li><li 
class="level-2"><a class="final" href="/system/anotherdatastore/"><span>Using 
Another Data Store</span></a></li></ul></li><li class="level-1"><a 
class="expandible" href="#"><span>PredictionIO Official 
Templates</span></a><ul><li class="level-2"><a class="final" 
href="/templates/"><span>Intro</span></a></li><li class="level-2"><a 
class="expandible" href="#"><span>Recommendation</span></a><ul><li 
class="level-3"><a class="final" 
href="/templates/recommendation/quickstart/"><span>Quick 
Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" href="/templates/
 recommendation/evaluation/"><span>Evaluation Explained</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/recommendation/how-to/"><span>How-To</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/recommendation/reading-custom-events/"><span>Read Custom 
Events</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/customize-data-prep/"><span>Customize Data 
Preparator</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/customize-serving/"><span>Customize 
Serving</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/training-with-implicit-preference/"><span>Train 
with Implicit Preference</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/blacklist-items/"><span>Filter Recommended 
Items by Blacklist in Query</span></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 class="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</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/similarproduct/how-to/"><span>How-To</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/similarproduct/multi-events-multi-algos/"><span>Multiple 
Events and Multiple Algorithms</span></a></li><li class="level-3"><a 
class="final" 
href="/templates/similarproduct/return-item-properties/"><span>Returns Item 
Properties</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarproduct/train-with-rate-event/"><span>Train with Rate 
Event</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarproduct/rid-user-set-event/"><span>Get Rid of Events 
for Users</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarproduct/recommended-user/"><span>Recommend 
Users</span></a></li></ul></li><li class="level-2"><a class="expandible" 
href="#"><span>Classification</span></a><ul><li class="level-3"><a clas
 s="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/tapster/"><span>Comics Recommendation Demo</span></a></li><li 
class="level-2"><a class="final" href="/demo/community/"><span>Community 
Contributed Demo</span></a></li><li class="level-2"><a class="final" 
href="/demo/textclassification/"><span>Text Classification Engine 
Tutorial</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="/community/"><span>Getting Involved</span></a><ul><li class="level-2"><a 
class="final" href="/community/contribute-code/"><span>Contribute 
Code</span></a></li><li class="level-2"><a class="final" 
href="/community/contribute-documentation/"><span>Contribute 
Documentation</span></a></li><li class="level-2"><a class="final" 
href="/community/contribute-sdk/"><span>Contribute a SDK</span></a></li><li 
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>Commu
 nity 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="https://www.apache.org/";><span>Apache Homepage</span></a></li><li 
class="level-2"><a class="final" 
href="https://www.apache.org/licenses/";><span>License</span></a></li><li 
class="level-2"><a class="final" 
href="https://www.apache.org/foundation/sponsorship.html";><span>Sponsorship</span></a></li><li
 class="level-2"><a class="final" 
href="https://www.apache.org/foundation/thanks.html";><span>Thanks</span></a></li><li
 class="level-2"><a class="final" 
href="https://www.apache.org/security/";><span>Security</span></a></li></ul></li></ul></nav></div><div
 class="col-md-9 col-sm-12"><div class="content-header hidden-md 
hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a 
href="#">Customizing an Engine</a><span 
class="spacer">&gt;</span></li><li><span class="last">Implement 
DASE</span></li></ul></div><div id="page-title"><h1>Implementing 
DASE</h1></div></div><div id="table-of-content-wrapper"><h5>On this 
page</h5><asid
 e id="table-of-contents"><ul> <li> <a href="#datasource">DataSource</a> <ul> 
<li> <a href="#readtraining">readTraining()</a> </li> <li> <a 
href="#using-peventstore-engine-api">Using PEventStore Engine API</a> </li> 
</ul> </li> <li> <a href="#preparator">Preparator</a> <ul> <li> <a 
href="#prepare">prepare()</a> </li> </ul> </li> <li> <a 
href="#algorithm">Algorithm</a> <ul> <li> <a href="#train">train()</a> </li> 
<li> <a href="#predict">predict()</a> </li> <li> <a 
href="#p2lalgorithm">P2LAlgorithm</a> </li> <li> <a 
href="#palgorithm">PAlgorithm</a> </li> <li> <a 
href="#using-leventstore-engine-api-in-predict">using LEventStore Engine API in 
predict()</a> </li> </ul> </li> <li> <a href="#serving">Serving</a> <ul> <li> 
<a href="#serve">serve()</a> </li> </ul> </li> </ul> </aside><hr/><a 
id="edit-page-link" 
href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/customize/dase.html.md.erb";><img
 src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a
 ></div><div class="content-header hidden-sm hidden-xs"><div id="breadcrumbs" 
 >class="hidden-sm hidden xs"><ul><li><a href="#">Customizing an 
 >Engine</a><span class="spacer">&gt;</span></li><li><span 
 >class="last">Implement DASE</span></li></ul></div><div 
 >id="page-title"><h1>Implementing DASE</h1></div></div><div class="content"> 
 ><p>This section gives you an overview of DASE components and how to implement 
 >them. You will find links to some engine templates for more concrete 
 >examples.</p><h1 id='datasource' 
 >class='header-anchors'>DataSource</h1><p>DataSource reads and selects useful 
 >data from the Event Store (data store of the Event Server) and returns 
 >TrainingData.</p><h2 id='readtraining()' 
 >class='header-anchors'>readTraining()</h2><p>You need to implment 
 >readTraining() of <a 
 >href="https://predictionio.apache.org/api/current/#org.apache.predictionio.controller.PDataSource";>PDataSource</a>,
 > where you can use the <a 
 >href="https://predictionio.apache.org/api/current/#org.apache.prediction
 io.data.store.PEventStore$">PEventStore Engine API</a> to read the events and 
create the TrainingData based on the events.</p><p>The following code example 
reads user &quot;view&quot; and &quot;buy&quot; item events, filters specific 
type of events for future processing and returns TrainingData 
accordingly.</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
@@ -51,7 +51,7 @@
   <span class="o">}</span>
 
 <span class="o">}</span>
-</pre></td></tr></tbody></table> </div> <h2 id='using-peventstore-engine-api' 
class='header-anchors'>Using PEventStore Engine API</h2><p>Please see <a 
href="https://predictionio.incubator.apache.org/datacollection/";>Event Server 
Overview</a> to understand <a 
href="https://predictionio.incubator.apache.org/datacollection/eventapi/";>EventAPI</a>
 and <a 
href="https://predictionio.incubator.apache.org/datacollection/eventmodel/";>event
 modeling</a>.</p><p>With <a 
href="https://predictionio.incubator.apache.org/api/current/#org.apache.predictionio.data.store.PEventStore$";>PEventStore
 Engine API</a>, you can easily read different events in DataSource and get the 
information you need.</p><p>For example, let&#39;s say you have events like the 
following:</p><div class="highlight json"><table style="border-spacing: 
0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre 
class="lineno">1
+</pre></td></tr></tbody></table> </div> <h2 id='using-peventstore-engine-api' 
class='header-anchors'>Using PEventStore Engine API</h2><p>Please see <a 
href="https://predictionio.apache.org/datacollection/";>Event Server 
Overview</a> to understand <a 
href="https://predictionio.apache.org/datacollection/eventapi/";>EventAPI</a> 
and <a href="https://predictionio.apache.org/datacollection/eventmodel/";>event 
modeling</a>.</p><p>With <a 
href="https://predictionio.apache.org/api/current/#org.apache.predictionio.data.store.PEventStore$";>PEventStore
 Engine API</a>, you can easily read different events in DataSource and get the 
information you need.</p><p>For example, let&#39;s say you have events like the 
following:</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
@@ -123,7 +123,7 @@
           <span class="k">throw</span> <span class="n">e</span>
       <span class="o">}</span>
     <span class="o">}</span>
-</pre></td></tr></tbody></table> </div> <p>If you have used special events 
<code>$set/$unset/$delete</code> setting entity&#39;s properties, you can 
retrieve it with <code>PEventStore.aggregateProperties()</code>.</p><p>Please 
see <a 
href="https://predictionio.incubator.apache.org/datacollection/eventmodel/";>event
 modeling</a> to understand usage of special <code>$set/$unset/$delete</code> 
events.</p><p>For example, the following code show how you could retrieve 
properties of the &quot;item&quot; entities:</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></tr></tbody></table> </div> <p>If you have used special events 
<code>$set/$unset/$delete</code> setting entity&#39;s properties, you can 
retrieve it with <code>PEventStore.aggregateProperties()</code>.</p><p>Please 
see <a href="https://predictionio.apache.org/datacollection/eventmodel/";>event 
modeling</a> to understand usage of special <code>$set/$unset/$delete</code> 
events.</p><p>For example, the following code show how you could retrieve 
properties of the &quot;item&quot; entities:</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
@@ -168,7 +168,7 @@
       <span class="o">}</span>
 
     <span class="o">}</span>
-</pre></td></tr></tbody></table> </div> <p>Example:</p> <ul> <li><a 
href="/templates/similarproduct/dase/#data">DataSource of Similar Product 
Template</a></li> </ul> <h1 id='preparator' 
class='header-anchors'>Preparator</h1><p>Preparator is responsible for 
pre-processing <code>TrainingData</code> for any necessary feature selection 
and data processing tasks and generate <code>PreparedData</code> which contains 
the data the Algorithm needs.</p><p>A few example usages of Preparator:</p> 
<ul> <li>Feature extraction</li> <li>Common pre-processing logic if you have 
multiple algorithms</li> <li>For simple cases, the Preparator may simply pass 
the same <code>TrainingData</code> as <code>PreparedData</code> for 
Algorithm.</li> </ul> <h2 id='prepare()' 
class='header-anchors'>prepare()</h2><p>You need to implement the 
<code>prepare()</code> method of <a 
href="https://predictionio.incubator.apache.org/api/current/#org.apache.predictionio.controller.PPreparator";>PPrepartor</a>
 to perform such t
 asks.</p><p>Example:</p> <ul> <li><a 
href="/templates/leadscoring/dase/#data">Preparator of Leading Scoring 
Template</a>: it pre-processes the TrainingData and generate the feature 
vectors needed for the algorithm.</li> <li><a 
href="/templates/similarproduct/dase/#data">Preparator of Similar Product 
Template</a>: it simply passes the TrainingData as PreparedData for the 
algorithm.</li> </ul> <h1 id='algorithm' 
class='header-anchors'>Algorithm</h1><p>The two methods of the Algorithm class 
are train() and predict():</p><h2 id='train()' 
class='header-anchors'>train()</h2><p>train() is responsible for training a 
predictive model. It is called when you run <code>pio train</code>. Apache 
PredictionIO (incubating) will store this model.</p><h2 id='predict()' 
class='header-anchors'>predict()</h2><p>predict() is responsible for using this 
model to make prediction. It is called when you send a JSON query to the 
engine. Note that predict() is called in real time.</p><p>Apache PredictionIO 
(inc
 ubating) supports two types of algorithms:</p> <ul> <li><strong><a 
href="https://predictionio.incubator.apache.org/api/current/#org.apache.predictionio.controller.P2LAlgorithm";>P2LAlgorithm</a></strong>:
 trains a Model which does not contain RDD</li> <li><strong><a 
href="https://predictionio.incubator.apache.org/api/current/#org.apache.predictionio.controller.PAlgorithm";>PAlgorithm</a></strong>:
 trains a Model which contains RDD</li> </ul> <h2 id='p2lalgorithm' 
class='header-anchors'>P2LAlgorithm</h2><p>For <code>P2LAlgorithm</code>, the 
Model is automatically serialized and persisted by Apache PredictionIO 
(incubating) after training.</p><p>Implementing <code>IPersistentModel</code> 
and <code>IPersistentModelLoader</code> is optional for 
P2LAlgorithm.</p><p>Example:</p> <ul> <li><a 
href="/templates/similarproduct/dase/#algorithm">Algorithm of Similar Product 
Template</a></li> </ul> <h2 id='palgorithm' 
class='header-anchors'>PAlgorithm</h2><p><code>PAlgorithm</code> should be used 
w
 hen your Model contains RDD. The model produced by <code>PAlgorithm</code> is 
not persisted by default. To persist the model, you need to do the 
following:</p> <ul> <li>The Model class should extend the 
<code>IPersistentModel</code> trait and implement the <code>save()</code> 
method for saving the model. The trait <code>IPersistentModel</code> requires a 
type parameter which is the class type of algorithm parameter.</li> 
<li>Implement a Model factory object which extends the 
<code>IPersistentModelLoader</code> trait and implement the 
<code>apply()</code> for loading the model. The trait 
<code>IPersistentModelLoader</code> requires two type parameters which are the 
types of algorithm parameter and the model produced by the algorithm.</li> 
</ul> <p>Example:</p> <ul> <li><a 
href="/templates/recommendation/dase/#algorithm">Algorithm of Recommendation 
Template</a>: it implements PAlgorithm and the IPersistentModel and 
IPersistentModelLoader.</li> <li><a href="/templates/vanilla/dase">Alg
 orithm of Vanilla Template</a>: it walks through example of P2LAlgorithm and 
PAlgorithm.</li> </ul> <h2 id='using-leventstore-engine-api-in-predict()' 
class='header-anchors'>using LEventStore Engine API in predict()</h2><p>You may 
use <a 
href="https://predictionio.incubator.apache.org/api/current/#org.apache.predictionio.data.store.LEventStore$";>LEventStore.findByEntity()</a>
 to retrieve events of a specific entity. For example, retrieve recent events 
of the user specified in the query) and use these recent events to make 
prediction in real time.</p><p>For example, the following code reads the recent 
10 view events of <code>query.user</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
+</pre></td></tr></tbody></table> </div> <p>Example:</p> <ul> <li><a 
href="/templates/similarproduct/dase/#data">DataSource of Similar Product 
Template</a></li> </ul> <h1 id='preparator' 
class='header-anchors'>Preparator</h1><p>Preparator is responsible for 
pre-processing <code>TrainingData</code> for any necessary feature selection 
and data processing tasks and generate <code>PreparedData</code> which contains 
the data the Algorithm needs.</p><p>A few example usages of Preparator:</p> 
<ul> <li>Feature extraction</li> <li>Common pre-processing logic if you have 
multiple algorithms</li> <li>For simple cases, the Preparator may simply pass 
the same <code>TrainingData</code> as <code>PreparedData</code> for 
Algorithm.</li> </ul> <h2 id='prepare()' 
class='header-anchors'>prepare()</h2><p>You need to implement the 
<code>prepare()</code> method of <a 
href="https://predictionio.apache.org/api/current/#org.apache.predictionio.controller.PPreparator";>PPrepartor</a>
 to perform such tasks.</p><
 p>Example:</p> <ul> <li><a href="/templates/leadscoring/dase/#data">Preparator 
of Leading Scoring Template</a>: it pre-processes the TrainingData and generate 
the feature vectors needed for the algorithm.</li> <li><a 
href="/templates/similarproduct/dase/#data">Preparator of Similar Product 
Template</a>: it simply passes the TrainingData as PreparedData for the 
algorithm.</li> </ul> <h1 id='algorithm' 
class='header-anchors'>Algorithm</h1><p>The two methods of the Algorithm class 
are train() and predict():</p><h2 id='train()' 
class='header-anchors'>train()</h2><p>train() is responsible for training a 
predictive model. It is called when you run <code>pio train</code>. Apache 
PredictionIO will store this model.</p><h2 id='predict()' 
class='header-anchors'>predict()</h2><p>predict() is responsible for using this 
model to make prediction. It is called when you send a JSON query to the 
engine. Note that predict() is called in real time.</p><p>Apache PredictionIO 
supports two types of algor
 ithms:</p> <ul> <li><strong><a 
href="https://predictionio.apache.org/api/current/#org.apache.predictionio.controller.P2LAlgorithm";>P2LAlgorithm</a></strong>:
 trains a Model which does not contain RDD</li> <li><strong><a 
href="https://predictionio.apache.org/api/current/#org.apache.predictionio.controller.PAlgorithm";>PAlgorithm</a></strong>:
 trains a Model which contains RDD</li> </ul> <h2 id='p2lalgorithm' 
class='header-anchors'>P2LAlgorithm</h2><p>For <code>P2LAlgorithm</code>, the 
Model is automatically serialized and persisted by Apache PredictionIO after 
training.</p><p>Implementing <code>IPersistentModel</code> and 
<code>IPersistentModelLoader</code> is optional for 
P2LAlgorithm.</p><p>Example:</p> <ul> <li><a 
href="/templates/similarproduct/dase/#algorithm">Algorithm of Similar Product 
Template</a></li> </ul> <h2 id='palgorithm' 
class='header-anchors'>PAlgorithm</h2><p><code>PAlgorithm</code> should be used 
when your Model contains RDD. The model produced by <code>PAlgorithm</
 code> is not persisted by default. To persist the model, you need to do the 
following:</p> <ul> <li>The Model class should extend the 
<code>IPersistentModel</code> trait and implement the <code>save()</code> 
method for saving the model. The trait <code>IPersistentModel</code> requires a 
type parameter which is the class type of algorithm parameter.</li> 
<li>Implement a Model factory object which extends the 
<code>IPersistentModelLoader</code> trait and implement the 
<code>apply()</code> for loading the model. The trait 
<code>IPersistentModelLoader</code> requires two type parameters which are the 
types of algorithm parameter and the model produced by the algorithm.</li> 
</ul> <p>Example:</p> <ul> <li><a 
href="/templates/recommendation/dase/#algorithm">Algorithm of Recommendation 
Template</a>: it implements PAlgorithm and the IPersistentModel and 
IPersistentModelLoader.</li> <li><a href="/templates/vanilla/dase">Algorithm of 
Vanilla Template</a>: it walks through example of P2LAlgori
 thm and PAlgorithm.</li> </ul> <h2 
id='using-leventstore-engine-api-in-predict()' class='header-anchors'>using 
LEventStore Engine API in predict()</h2><p>You may use <a 
href="https://predictionio.apache.org/api/current/#org.apache.predictionio.data.store.LEventStore$";>LEventStore.findByEntity()</a>
 to retrieve events of a specific entity. For example, retrieve recent events 
of the user specified in the query) and use these recent events to make 
prediction in real time.</p><p>For example, the following code reads the recent 
10 view events of <code>query.user</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
@@ -211,7 +211,7 @@
         <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">"Error when read recent events: ${e}"</span><span class="o">)</span>
         <span class="k">throw</span> <span class="n">e</span>
     <span class="o">}</span>
-</pre></td></tr></tbody></table> </div> <p>Example:</p> <ul> <li><a 
href="/templates/ecommercerecommendation/dase#algorithm">Algorithm of 
E-Commerce Recommendation template</a>: LEventStore.findByEntity() is used to 
retrieve all items seen by the user and filter them from recommendation in 
predict().</li> </ul> <h1 id='serving' class='header-anchors'>Serving</h1><h2 
id='serve()' class='header-anchors'>serve()</h2><p>You need to implement the 
serve() method of the class <a 
href="https://predictionio.incubator.apache.org/api/current/#org.apache.predictionio.controller.LServing";>LServing</a>.
 The serve() method processes predicted result. It is also responsible for 
combining multiple predicted results into one if you have more than one 
predictive model.</p><p>Example:</p> <ul> <li><a 
href="/templates/similarproduct/dase/#serving">Serving of Similar Product 
Template</a>: It simply returns the predicted result</li> <li><a 
href="/templates/similarproduct/multi-events-multi-algos/">Serving
  of multi-algorithm examples of Similar Product Template</a>: It combines the 
result of multiple algorithms and return</li> </ul> 
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+</pre></td></tr></tbody></table> </div> <p>Example:</p> <ul> <li><a 
href="/templates/ecommercerecommendation/dase#algorithm">Algorithm of 
E-Commerce Recommendation template</a>: LEventStore.findByEntity() is used to 
retrieve all items seen by the user and filter them from recommendation in 
predict().</li> </ul> <h1 id='serving' class='header-anchors'>Serving</h1><h2 
id='serve()' class='header-anchors'>serve()</h2><p>You need to implement the 
serve() method of the class <a 
href="https://predictionio.apache.org/api/current/#org.apache.predictionio.controller.LServing";>LServing</a>.
 The serve() method processes predicted result. It is also responsible for 
combining multiple predicted results into one if you have more than one 
predictive model.</p><p>Example:</p> <ul> <li><a 
href="/templates/similarproduct/dase/#serving">Serving of Similar Product 
Template</a>: It simply returns the predicted result</li> <li><a 
href="/templates/similarproduct/multi-events-multi-algos/">Serving of multi-
 algorithm examples of Similar Product Template</a>: It combines the result of 
multiple algorithms and return</li> </ul> </div></div></div></div><footer><div 
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