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 an></a></li><li class="level-3"><a class="final" 
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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">Us
 ing 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;</s
 pan></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="https://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 re
 ads 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
+5
+6
+7
+8
+9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20
+21
+22
+23
+24
+25
+26
+27</pre></td><td class="code"><pre><span class="k">class</span> <span 
class="nc">DataSource</span><span class="o">(</span><span class="k">val</span> 
<span class="n">dsp</span><span class="k">:</span> <span 
class="kt">DataSourceParams</span><span class="o">)</span>
+  <span class="k">extends</span> <span class="nc">PDataSource</span><span 
class="o">[</span><span class="kt">TrainingData</span>,
+      <span class="kt">EmptyEvaluationInfo</span>, <span 
class="kt">Query</span>, <span class="kt">EmptyActualResult</span><span 
class="o">]</span> <span class="o">{</span>
+
+  <span class="nd">@transient</span> <span class="k">lazy</span> <span 
class="k">val</span> <span class="n">logger</span> <span class="k">=</span> 
<span class="nc">Logger</span><span class="o">[</span><span 
class="kt">this.</span><span class="k">type</span><span class="o">]</span>
+
+  <span class="k">override</span>
+  <span class="k">def</span> <span class="n">readTraining</span><span 
class="o">(</span><span class="n">sc</span><span class="k">:</span> <span 
class="kt">SparkContext</span><span class="o">)</span><span class="k">:</span> 
<span class="kt">TrainingData</span> <span class="o">=</span> <span 
class="o">{</span>
+
+    <span class="k">val</span> <span class="n">eventsRDD</span><span 
class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span 
class="kt">Event</span><span class="o">]</span> <span class="k">=</span> <span 
class="nc">PEventStore</span><span class="o">.</span><span 
class="n">find</span><span class="o">(</span>
+      <span class="n">appName</span> <span class="k">=</span> <span 
class="n">dsp</span><span class="o">.</span><span class="n">appName</span><span 
class="o">,</span>
+      <span class="n">entityType</span> <span class="k">=</span> <span 
class="nc">Some</span><span class="o">(</span><span 
class="s">"user"</span><span class="o">),</span>
+      <span class="n">eventNames</span> <span class="k">=</span> <span 
class="nc">Some</span><span class="o">(</span><span class="nc">List</span><span 
class="o">(</span><span class="s">"view"</span><span class="o">,</span> <span 
class="s">"buy"</span><span class="o">)),</span>
+      <span class="c1">// targetEntityType is optional field of an event.
+</span>      <span class="n">targetEntityType</span> <span class="k">=</span> 
<span class="nc">Some</span><span class="o">(</span><span 
class="nc">Some</span><span class="o">(</span><span 
class="s">"item"</span><span class="o">)))(</span><span 
class="n">sc</span><span class="o">)</span>
+      <span class="o">.</span><span class="n">cache</span><span 
class="o">()</span>
+
+    <span class="k">val</span> <span class="n">viewEventsRDD</span><span 
class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span 
class="kt">ViewEvent</span><span class="o">]</span> <span class="k">=</span> 
<span class="n">eventsRDD</span>
+      <span class="o">.</span><span class="n">filter</span> <span 
class="o">{</span> <span class="n">event</span> <span class="k">=&gt;</span> 
<span class="n">event</span><span class="o">.</span><span 
class="n">event</span> <span class="o">==</span> <span class="s">"view"</span> 
<span class="o">}</span>
+      <span class="o">.</span><span class="n">map</span> <span 
class="o">{</span> <span class="o">...</span> <span class="o">}</span>
+
+    <span class="o">...</span>
+
+    <span class="k">new</span> <span class="nc">TrainingData</span><span 
class="o">(...)</span>
+  <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
+2
+3
+4
+5
+6
+7
+8
+9
+10
+11
+12
+13
+14</pre></td><td class="code"><pre><span class="p">{</span><span class="w">
+  </span><span class="s2">"event"</span><span class="p">:</span><span 
class="w"> </span><span class="s2">"myEvent"</span><span 
class="p">,</span><span class="w">
+  </span><span class="s2">"entityType"</span><span class="p">:</span><span 
class="w"> </span><span class="s2">"user"</span><span class="p">,</span><span 
class="w">
+  </span><span class="s2">"entityId"</span><span class="p">:</span><span 
class="w"> </span><span class="s2">"u0"</span><span class="p">,</span><span 
class="w">
+  </span><span class="s2">"targetEntityType"</span><span 
class="p">:</span><span class="w"> </span><span class="s2">"item"</span><span 
class="p">,</span><span class="w">
+  </span><span class="s2">"targetEntityId"</span><span class="p">:</span><span 
class="w"> </span><span class="s2">"i0"</span><span class="p">,</span><span 
class="w">
+  </span><span class="s2">"properties"</span><span class="w"> </span><span 
class="p">:</span><span class="w"> </span><span class="p">{</span><span 
class="w">
+    </span><span class="s2">"a"</span><span class="w"> </span><span 
class="p">:</span><span class="w"> </span><span class="mi">3</span><span 
class="p">,</span><span class="w">
+    </span><span class="s2">"b"</span><span class="w"> </span><span 
class="p">:</span><span class="w"> </span><span 
class="s2">"some_string"</span><span class="p">,</span><span class="w">
+    </span><span class="s2">"c"</span><span class="w"> </span><span 
class="p">:</span><span class="w"> </span><span class="p">[</span><span 
class="s2">"a"</span><span class="p">,</span><span class="w"> </span><span 
class="s2">"b"</span><span class="p">,</span><span class="w"> </span><span 
class="s2">"c"</span><span class="p">],</span><span class="w">
+    </span><span class="s2">"d"</span><span class="w"> </span><span 
class="p">:</span><span class="w"> </span><span class="p">[</span><span 
class="mf">1.2</span><span class="p">,</span><span class="w"> </span><span 
class="mf">3.4</span><span class="p">,</span><span class="w"> </span><span 
class="mf">5.6</span><span class="p">],</span><span class="w">
+    </span><span class="s2">"e"</span><span class="w"> </span><span 
class="p">:</span><span class="w"> </span><span class="mi">6</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>Then following code could 
read these events and extract the properties field of the event and convert it 
to a <code>MyEvent</code> object.</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</pre></td><td class="code"><pre>  <span class="k">val</span> <span 
class="n">myEvents</span><span class="k">:</span> <span 
class="kt">RDD</span><span class="o">[</span><span 
class="kt">MyEvent</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">"myEvent"</span><span class="o">)),</span>
+      <span class="c1">// targetEntityType is optional field of an event.
+</span>      <span class="n">targetEntityType</span> <span class="k">=</span> 
<span class="nc">Some</span><span class="o">(</span><span 
class="nc">Some</span><span class="o">(</span><span 
class="s">"item"</span><span class="o">)))(</span><span 
class="n">sc</span><span class="o">)</span>
+    <span class="o">.</span><span class="n">map</span> <span 
class="o">{</span> <span class="n">event</span> <span class="k">=&gt;</span>
+      <span class="k">try</span> <span class="o">{</span>
+        <span class="nc">MyEvent</span><span class="o">(</span>
+          <span class="n">entityId</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">targetEntityId</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">a</span> <span class="k">=</span> <span 
class="n">event</span><span class="o">.</span><span 
class="n">properties</span><span class="o">.</span><span 
class="n">get</span><span class="o">[</span><span class="kt">Int</span><span 
class="o">](</span><span class="s">"a"</span><span class="o">),</span>
+          <span class="n">b</span> <span class="k">=</span> <span 
class="n">event</span><span class="o">.</span><span 
class="n">properties</span><span class="o">.</span><span 
class="n">get</span><span class="o">[</span><span class="kt">String</span><span 
class="o">](</span><span class="s">"b"</span><span class="o">),</span>
+          <span class="n">c</span> <span class="k">=</span> <span 
class="n">event</span><span class="o">.</span><span 
class="n">properties</span><span class="o">.</span><span 
class="n">get</span><span class="o">[</span><span class="kt">List</span><span 
class="o">[</span><span class="kt">String</span><span class="o">]](</span><span 
class="s">"c"</span><span class="o">),</span>
+          <span class="n">d</span> <span class="k">=</span> <span 
class="n">event</span><span class="o">.</span><span 
class="n">properties</span><span class="o">.</span><span 
class="n">get</span><span class="o">[</span><span class="kt">List</span><span 
class="o">[</span><span class="kt">Double</span><span class="o">]](</span><span 
class="s">"d"</span><span class="o">),</span>
+          <span class="n">e</span> <span class="k">=</span> <span 
class="n">event</span><span class="o">.</span><span 
class="n">properties</span><span class="o">.</span><span 
class="n">getOpt</span><span class="o">[</span><span class="kt">Int</span><span 
class="o">](</span><span class="s">"e"</span><span class="o">)</span> <span 
class="c1">// use getOpt for optional data
+</span>        <span class="o">)</span>
+      <span class="o">}</span> <span class="k">catch</span> <span 
class="o">{</span>
+        <span class="k">case</span> <span class="n">e</span><span 
class="k">:</span> <span class="kt">Exception</span> <span 
class="o">=&gt;</span>
+          <span class="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}. Exception: ${e}."</span><span 
class="o">)</span>
+          <span class="k">throw</span> <span class="n">e</span>
+      <span class="o">}</span>
+    <span class="o">}</span>
+</pre></td></tr></tbody></table> </div> <p>If you have used special events 
<code>$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
+2
+3
+4
+5
+6
+7
+8
+9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20
+21
+22
+23</pre></td><td class="code"><pre>    <span class="c1">// create a RDD of 
(entityID, Item)
+</span>    <span class="k">val</span> <span class="n">itemsRDD</span><span 
class="k">:</span> <span class="kt">RDD</span><span class="o">[(</span><span 
class="kt">String</span>, <span class="kt">Item</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="n">sc</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">=&gt;</span>
+
+      <span class="k">try</span> <span class="o">{</span>
+        <span class="k">val</span> <span class="n">item</span> <span 
class="k">=</span> <span class="nc">Item</span><span class="o">(</span>
+          <span class="n">a</span> <span class="k">=</span> <span 
class="n">preopties</span><span class="o">.</span><span 
class="n">get</span><span class="o">[</span><span class="kt">Int</span><span 
class="o">](</span><span class="s">"a"</span><span class="o">),</span>
+          <span class="n">b</span> <span class="k">=</span> <span 
class="n">properties</span><span class="o">.</span><span 
class="n">get</span><span class="o">[</span><span class="kt">String</span><span 
class="o">](</span><span class="s">"b"</span><span class="o">),</span>
+          <span class="n">c</span> <span class="k">=</span> <span 
class="n">properties</span><span class="o">.</span><span 
class="n">get</span><span class="o">[</span><span class="kt">List</span><span 
class="o">[</span><span class="kt">String</span><span class="o">]](</span><span 
class="s">"c"</span><span class="o">),</span>
+          <span class="n">d</span> <span class="k">=</span> <span 
class="n">properties</span><span class="o">.</span><span 
class="n">get</span><span class="o">[</span><span class="kt">List</span><span 
class="o">[</span><span class="kt">Double</span><span class="o">]](</span><span 
class="s">"d"</span><span class="o">),</span>
+          <span class="n">e</span> <span class="k">=</span> <span 
class="n">properties</span><span class="o">.</span><span 
class="n">getOpt</span><span class="o">[</span><span class="kt">Int</span><span 
class="o">](</span><span class="s">"e"</span><span class="o">)</span> <span 
class="c1">// use getOpt for optional data
+</span>        <span class="o">)</span>
+
+        <span class="o">(</span><span class="n">entityId</span><span 
class="o">,</span> <span class="n">item</span><span class="o">)</span>
+      <span class="o">}</span> <span class="k">catch</span> <span 
class="o">{</span>
+        <span class="k">case</span> <span class="n">e</span><span 
class="k">:</span> <span class="kt">Exception</span> <span 
class="o">=&gt;</span>
+          <span class="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 ${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>
+</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
+2
+3
+4
+5
+6
+7
+8
+9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20
+21
+22</pre></td><td class="code"><pre>    <span class="k">val</span> <span 
class="n">recentEvents</span> <span class="k">=</span> <span 
class="k">try</span> <span class="o">{</span>
+      <span class="nc">LEventStore</span><span class="o">.</span><span 
class="n">findByEntity</span><span class="o">(</span>
+        <span class="n">appName</span> <span class="k">=</span> <span 
class="n">ap</span><span class="o">.</span><span class="n">appName</span><span 
class="o">,</span>
+        <span class="c1">// entityType and entityId is specified for fast 
lookup
+</span>        <span class="n">entityType</span> <span class="k">=</span> 
<span class="s">"user"</span><span class="o">,</span>
+        <span class="n">entityId</span> <span class="k">=</span> <span 
class="n">query</span><span class="o">.</span><span class="n">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">"view"</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">limit</span> <span class="k">=</span> <span 
class="nc">Some</span><span class="o">(</span><span class="mi">10</span><span 
class="o">),</span>
+        <span class="n">latest</span> <span class="k">=</span> <span 
class="kc">true</span><span class="o">,</span>
+        <span class="c1">// set time limit to avoid super long DB access
+</span>        <span class="n">timeout</span> <span class="k">=</span> <span 
class="nc">Duration</span><span class="o">(</span><span 
class="mi">200</span><span class="o">,</span> <span 
class="s">"millis"</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">scala.concurrent.TimeoutException</span> 
<span class="o">=&gt;</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">"Timeout when read recent events."</span> <span class="o">+</span>
+          <span class="n">s</span><span class="s">" Empty list is used. 
${e}"</span><span class="o">)</span>
+        <span class="nc">Iterator</span><span class="o">[</span><span 
class="kt">Event</span><span class="o">]()</span>
+      <span class="k">case</span> <span class="n">e</span><span 
class="k">:</span> <span class="kt">Exception</span> <span 
class="o">=&gt;</span>
+        <span class="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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 ul><li class="level-1"><a class="expandible" href="/"><span>Apache 
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(incubating)</span></a></li></ul></li><li class="level-1"><a class="expandible" 
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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" 
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class="level-2"><a class="final" href="/deploy/"><span>Deploying as a Web 
Service</span></a></li><li class="level-2"><a class="final" 
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class="level-2"><a class="final" href="/deploy/monitoring/">
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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 active" 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/"><spa
 n>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="/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/ecomm
 ercerecommendation/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="lev
 el-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><l
 i 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>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>FAQ
 s</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/license
 s/"><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">Learning 
DASE</span></li></ul></div><div id="page-title"><h1>Learning 
DASE</h1></div></div><div id="table-of-content-wrapper"><h5>On this 
page</h5><aside id="table-of-contents"><ul> <li> <a 
href="#the-roles-of-an-engine">The Roles of an Engine</a> </li> <li> <a 
href="#examples-of-dase">Examples of DASE</a> </li> </ul> </aside><hr/><a 
id="edit-page
 -link" 
href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/customize/index.html.md";><img
 src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div 
class="content-header hidden-sm hidden-xs"><div id="breadcrumbs" 
class="hidden-sm hidden xs"><ul><li><a href="#">Customizing an Engine</a><span 
class="spacer">&gt;</span></li><li><span class="last">Learning 
DASE</span></li></ul></div><div id="page-title"><h1>Learning 
DASE</h1></div></div><div class="content"> <p>The code of an engine consists of 
D-A-S-E components:</p><h3 id='[d]-data-source-and-data-preparator' 
class='header-anchors'>[D] Data Source and Data Preparator</h3><p>Data Source 
reads data from an input source and transforms it into a desired format. Data 
Preparator preprocesses the data and forwards it to the algorithm for model 
training.</p><h3 id='[a]-algorithm' class='header-anchors'>[A] 
Algorithm</h3><p>The Algorithm component includes the Machine Learning 
algorithm, and t
 he settings of its parameters, determines how a predictive model is 
constructed.</p><h3 id='[s]-serving' class='header-anchors'>[S] 
Serving</h3><p>The Serving component takes prediction <em>queries</em> and 
returns prediction results. If the engine has multiple algorithms, Serving will 
combine the results into one. Additionally, business-specific logic can be 
added in Serving to further customize the final returned results.</p><h3 
id='[e]-evaluation-metrics' class='header-anchors'>[E] Evaluation 
Metrics</h3><p>An Evaluation Metric quantifies prediction accuracy with a 
numerical score. It can be used for comparing algorithms or algorithm parameter 
settings.</p> <blockquote> <p>Apache PredictionIO (incubating) helps you 
modularize these components so you can build, for example, several Serving 
components for an Engine. You will be able to choose which one to be deployed 
when you create an Engine.</p></blockquote> <p><img alt="Engine Overview" 
src="/images/engineinstance-overview-c6668
 657.png"/></p><h2 id='the-roles-of-an-engine' class='header-anchors'>The Roles 
of an Engine</h2><p>The main functions of an engine are:</p> <ul> <li>Train a 
model using the training data and be deployed as a web service</li> <li>Respond 
to prediction query in real-time</li> </ul> <p>An engine puts all DASE 
components into a deployable state by specifying:</p> <ul> <li><p>One Data 
Source</p></li> <li><p>One Data Preparator</p></li> <li><p>One or more 
Algorithm(s)</p></li> <li><p>One Serving</p></li> </ul> <div 
class="alert-message info"><p>If more than one algorithm is specified, each of 
their model prediction results will be passed to Serving for 
ensembling.</p></div><p>Each Engine processes data and constructs predictive 
models independently. Therefore, every engine serves its own set of prediction 
results. For example, you may deploy two engines for your mobile application: 
one for recommending news to users and another one for suggesting new friends 
to users.</p><h3 id='training-
 a-model---the-dase-view' class='header-anchors'>Training a Model - The DASE 
View</h3><p>The following graph shows the workflow of DASE components when 
<code>pio train</code> is run.</p><p><img alt="Engine Overview" 
src="/images/engine-training-93bc1b69.png"/></p><h3 
id='respond-to-prediction-query---the-dase-view' class='header-anchors'>Respond 
to Prediction Query - The DASE View</h3><p>The following graph shows the 
workflow of DASE components when a REST query is received by a deployed 
engine.</p><p><img alt="Engine Overview" 
src="/images/engine-query-8d7311ff.png"/></p><p>Please see <a 
href="/customize/dase">Implement DASE</a> for DASE implementation 
details.</p><p>Please refer to following templates and their how-to guides for 
concrete examples.</p><h2 id='examples-of-dase' class='header-anchors'>Examples 
of DASE</h2> <ul> <li><a href="/templates/recommendation/dase/">DASE of 
Recommendation Template</a></li> <li><a 
href="/templates/similarproduct/dase/">DASE of Similar Product Te
 mplate</a></li> <li><a href="/templates/classification/dase/">DASE of 
Classification Template</a></li> <li><a 
href="/templates/leadscoring/dase/">DASE of Lead Scoring Template</a></li> 
</ul> </div></div></div></div><footer><div class="container"><div 
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