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SDKs</span></a></li></ul></li></ul></li><li class="level-1"><a 
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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" 
href="/cli/#engine-commands"><span>Engine Command-line 
Interface</span></a></li><li class="level-2"><a class="final" href="/b
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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 
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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>Collec
 ting 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="/cli/#event-server-commands"><span>Event 
Server Command-line Interface</span></a></li><li class="level-2"><a 
class="final" href="/datacollection/eventapi/"><span>Collecting Data with 
REST/SDKs</span></a></li><li class="level-2"><a class="final" 
href="/datacollection/eventmodel/"><span>Events Modeling</span></a></li><li 
class="level-2"><a class="final" 
href="/datacollection/webhooks/"><span>Unifying Multichannel Data with 
Webhooks</span></a></li><li class="level-2"><a class="final" 
href="/datacollection/channel/"><span>Channel</span></a></li><li 
class="level-2"><a class="final" 
href="/datacollection/batchimport/"><span>Importing Data in 
Batch</span></a></li><li class="level-2"><a class="final" 
href="/datacollection/analytics/"><span>Using Analytics 
Tools</span></a></li></ul></li><li class="level-1"><a 
 class="expandible" 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="/evaluati
 on/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/re
 commendation/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>Qui
 ck 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>Cl
 assification</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 c
 lass="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 
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class="level-2"><a class="final" 
href="/community/contribute-code/"><span>Contribute Code</span></a></li><li 
class="level-2"><a class="final" 
href="/community/contribute-documentation/"><span>Contribute 
Documentation</span></a></li><li class="level-2"><a class="final" 
href="/community/contribute-sdk/"><span>Contribute a SDK</span></a></li><li 
class="level-2"><a class="final" 
href="/community/contribute-webhook/"><span>Contribute a 
Webhook</span></a></li><li class="level-2"><a
  class="final" href="/community/projects/"><span>Community 
Projects</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><span>Getting Help</span></a><ul><li class="level-2"><a class="final" 
href="/resources/faq/"><span>FAQs</span></a></li><li class="level-2"><a 
class="final" href="/support/"><span>Support</span></a></li></ul></li><li 
class="level-1"><a class="expandible" 
href="#"><span>Resources</span></a><ul><li class="level-2"><a class="final" 
href="/resources/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></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 cod
 e 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 the 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 E
 valuation 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-c6668657.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="Engi
 ne 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 
Template</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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 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">Troubleshooting Engine 
Development</span></li></ul></div><div id="page-title"><h1>Engine Development - 
Troubleshoot</h1></div></div><div id="table-of-content-wrapper"><h5>On this 
page</h5><aside id="table-of-contents"><ul> <li> <a 
href="#stop-training-between-stages">Stop Training between Stages</a> </li> 
<li> <a href="#sanity-check">Sanity Check</a> </li> <li> <a 
href="#engine-status-page">Engine Status Page</a> </li> <li> <a 
href="#pio-shell">pio-shell</a> </li> </ul> </aside><hr/><a id="edit-page-link" 
href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/customize/troubleshooting.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">Troubleshooting Engine 
Development</span></li></ul></div><div id="page-title"><h1>Engine Development - 
Troubleshoot</h1></div></div><div class="content"> <p>Apache PredictionIO 
(incubating) provides the following features to help you debug engines during 
development cycle.</p><h2 id='stop-training-between-stages' 
class='header-anchors'>Stop Training between Stages</h2><p>By default <code>pio 
train</code> runs through the whole training process including <a 
href="/templates/recommendation/dase/">DataSource, Preparator and 
Algorithm</a>. To speed up the development and debug cycle, you can stop the 
process after each stage to verify it has completed correctly.</p><p>If you 
have modified DataSource and want to confirm the TrainingData is generated as 
expected, you can run <code>pio train</code> with 
<code>--stop-after-read</code> option:</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>pio train --stop-after-read
+</pre></td></tr></tbody></table> </div> <p>This would stop the training 
process after the TrainingData is generated.</p><p>For example, if you are 
running <a href="/templates/recommendation/quickstart/">Recommendation 
Template</a>, you should see the the training process stops after the 
TrainingData is printed.</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</pre></td><td class="code"><pre><span class="o">[</span>INFO] <span 
class="o">[</span>CoreWorkflow<span class="nv">$]</span> TrainingData:
+<span class="o">[</span>INFO] <span class="o">[</span>CoreWorkflow<span 
class="nv">$]</span> ratings: <span class="o">[</span>1501] <span 
class="o">(</span>List<span class="o">(</span>Rating<span 
class="o">(</span>3,0,4.0<span class="o">)</span>, Rating<span 
class="o">(</span>3,1,4.0<span class="o">))</span>...<span class="o">)</span>
+...
+<span class="o">[</span>INFO] <span class="o">[</span>CoreWorkflow<span 
class="nv">$]</span> Training interrupted by 
org.apache.predictionio.workflow.StopAfterReadInterruption.
+</pre></td></tr></tbody></table> </div> <p>Similarly, you can stop the 
training after the Preparator phase by using --stop-after-prepare option and it 
would stop after PreparedData is generated:</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>pio train --stop-after-prepare
+</pre></td></tr></tbody></table> </div> <h2 id='sanity-check' 
class='header-anchors'>Sanity Check</h2><p>You can extend a trait 
<code>SanityCheck</code> and implement the method <code>sanityCheck()</code> 
with your error checking code. The <code>sanityCheck()</code> is called when 
the data is generated. This can be applied to <code>TrainingData</code>, 
<code>PreparedData</code> and the <code>Model</code> classes, which are outputs 
of DataSource&#39;s <code>readTraining()</code>, Preparator&#39;s 
<code>prepare()</code> and Algorithm&#39;s <code>train()</code> methods, 
respectively.</p><p>For example, one frequent error with the Recommendation 
Template is that the TrainingData is empty because the DataSource is not 
reading data correctly. You can add the check of empty data inside the 
<code>sanityCheck()</code> function. You can easily add other checking logic 
into the <code>sanityCheck()</code> function based on your own needs. Also, If 
you implement <code>toString()</code> method in
  your TrainingData. You can call <code>toString()</code> inside 
<code>sanityCheck()</code> to print out some data for visual 
checking.</p><p>For example, to print TrainingData to console and check if the 
<code>ratings</code> is empty, you can do the following:</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</pre></td><td class="code"><pre><span class="k">import</span> <span 
class="nn">org.apache.predictionio.controller.SanityCheck</span> <span 
class="c1">// ADDED
+</span>
+<span class="k">class</span> <span class="nc">TrainingData</span><span 
class="o">(</span>
+  <span class="k">val</span> <span class="n">ratings</span><span 
class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span 
class="kt">Rating</span><span class="o">]</span>
+<span class="o">)</span> <span class="k">extends</span> <span 
class="nc">Serializable</span> <span class="k">with</span> <span 
class="nc">SanityCheck</span> <span class="o">{</span> <span class="c1">// 
EXTEND SanityCheck
+</span>  <span class="k">override</span> <span class="k">def</span> <span 
class="n">toString</span> <span class="k">=</span> <span class="o">{</span>
+    <span class="n">s</span><span class="s">"ratings: [${ratings.count()}] 
(${ratings.take(2).toList}...)"</span>
+  <span class="o">}</span>
+
+  <span class="c1">// IMPLEMENT sanityCheck()
+</span>  <span class="k">override</span> <span class="k">def</span> <span 
class="n">sanityCheck</span><span class="o">()</span><span class="k">:</span> 
<span class="kt">Unit</span> <span class="o">=</span> <span class="o">{</span>
+    <span class="n">println</span><span class="o">(</span><span 
class="n">toString</span><span class="o">())</span>
+    <span class="c1">// add your other checking here
+</span>    <span class="n">require</span><span class="o">(!</span><span 
class="n">ratings</span><span class="o">.</span><span 
class="n">take</span><span class="o">(</span><span class="mi">1</span><span 
class="o">).</span><span class="n">isEmpty</span><span class="o">,</span> <span 
class="n">s</span><span class="s">"ratings cannot be empty!"</span><span 
class="o">)</span>
+  <span class="o">}</span>
+<span class="o">}</span>
+</pre></td></tr></tbody></table> </div> <p>You may also use together with 
--stop-after-read flag to debug the DataSource:</p><div class="highlight 
shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" 
style="text-align: right"><pre class="lineno">1
+2</pre></td><td class="code"><pre>pio build
+pio train --stop-after-read
+</pre></td></tr></tbody></table> </div> <p>If your data is empty, you should 
see the following error thrown by the <code>sanityCheck()</code> 
function:</p><div class="highlight shell"><table style="border-spacing: 
0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre 
class="lineno">1
+2
+3
+4
+5
+6
+7
+8
+9</pre></td><td class="code"><pre><span class="o">[</span>INFO] <span 
class="o">[</span>CoreWorkflow<span class="nv">$]</span> Performing data sanity 
check on training data.
+<span class="o">[</span>INFO] <span class="o">[</span>CoreWorkflow<span 
class="nv">$]</span> org.template.recommendation.TrainingData supports data 
sanity check. Performing check.
+Exception <span class="k">in </span>thread <span class="s2">"main"</span> 
java.lang.IllegalArgumentException: requirement failed: ratings cannot be empty!
+    at scala.Predef<span class="nv">$.</span>require<span 
class="o">(</span>Predef.scala:233<span class="o">)</span>
+    at org.template.recommendation.TrainingData.sanityCheck<span 
class="o">(</span>DataSource.scala:73<span class="o">)</span>
+    at org.apache.predictionio.workflow.CoreWorkflow<span 
class="nv">$$</span>anonfun<span 
class="nv">$runTypelessContext$7</span>.apply<span 
class="o">(</span>Workflow.scala:474<span class="o">)</span>
+    at org.apache.predictionio.workflow.CoreWorkflow<span 
class="nv">$$</span>anonfun<span 
class="nv">$runTypelessContext$7</span>.apply<span 
class="o">(</span>Workflow.scala:465<span class="o">)</span>
+    at scala.collection.immutable.Map<span 
class="nv">$Map1</span>.foreach<span class="o">(</span>Map.scala:109<span 
class="o">)</span>
+  ...
+</pre></td></tr></tbody></table> </div> <p>You can specify the 
<code>--skip-sanity-check</code> option to turn off sanityCheck:</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>pio train --stop-after-read --skip-sanity-check
+</pre></td></tr></tbody></table> </div> <p>You should see the checking is 
skipped such as the following output:</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</pre></td><td class="code"><pre><span class="o">[</span>INFO] <span 
class="o">[</span>CoreWorkflow<span class="nv">$]</span> Data sanity checking 
is off.
+<span class="o">[</span>INFO] <span class="o">[</span>CoreWorkflow<span 
class="nv">$]</span> Data Source
+...
+<span class="o">[</span>INFO] <span class="o">[</span>CoreWorkflow<span 
class="nv">$]</span> Training interrupted by 
org.apache.predictionio.workflow.StopAfterReadInterruption.
+</pre></td></tr></tbody></table> </div> <h2 id='engine-status-page' 
class='header-anchors'>Engine Status Page</h2><p>After run <code>pio 
deploy</code>, you can access the engine status page by go to same URL and port 
of the deployed engine with your browser, which is &quot;<a 
href="http://localhost:8000";>http://localhost:8000</a>&quot; by default. In the 
engine status page, you can find the Engine information, and parameters of each 
DASE components. In particular, you can also see the &quot;Model&quot; trained 
by the algorithm based on how <code>toString()</code> method is implemented in 
the Algorithm&#39;s Model class.</p><h2 id='pio-shell' 
class='header-anchors'>pio-shell</h2><p>Apache PredictionIO (incubating) also 
provides <code>pio-shell</code> in which you can easily access Apache 
PredictionIO (incubating) API, Spark context and Spark API for quickly testing 
code or debugging purposes.</p><p>To bring up the shell, simply run:</p><div 
class="highlight shell"><table style="borde
 r-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre 
class="lineno">1</pre></td><td class="code"><pre><span class="gp">$ 
</span>pio-shell --with-spark
+</pre></td></tr></tbody></table> </div> <p>(<code>pio-shell</code> is 
available inside <code>bin/</code> directory of installed Apache PredictionIO 
(incubating) directory, you should be able to access it if you have added 
PredictionIO/bin into your environment variable <code>PATH</code>)</p><p>Note 
that the Spark context is available as variable <code>sc</code> inside the 
shell.</p><p>For example, to get the events of <code>MyApp1</code> using 
PEventStore API inside the pio-shell and collect them into an array 
<code>c</code>. run the following in the shell:</p><div class="highlight 
shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" 
style="text-align: right"><pre class="lineno">1
+2
+3</pre></td><td class="code"><pre><span class="gp">&gt; </span>import 
org.apache.predictionio.data.store.PEventStore
+<span class="gp">&gt; </span>val eventsRDD <span class="o">=</span> 
PEventStore.find<span class="o">(</span><span class="nv">appName</span><span 
class="o">=</span><span class="s2">"MyApp1"</span><span 
class="o">)(</span>sc<span class="o">)</span>
+<span class="gp">&gt; </span>val c <span class="o">=</span> 
eventsRDD.collect<span class="o">()</span>
+</pre></td></tr></tbody></table> </div> <p>Then you should see following 
returned in the shell:</p><div class="highlight shell"><table 
style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: 
right"><pre class="lineno">1
+2
+3</pre></td><td class="code"><pre>...
+15/05/18 14:24:42 INFO DAGScheduler: Job 0 finished: collect at 
&lt;console&gt;:24, took 1.850779 s
+c: Array[org.apache.predictionio.data.storage.Event] <span class="o">=</span> 
Array<span class="o">(</span>Event<span class="o">(</span><span 
class="nv">id</span><span class="o">=</span>Some<span 
class="o">(</span>AaQUUBsFZxteRpDV_7fDGQAAAU1ZfRW1tX9LSWdZSb0<span 
class="o">)</span>,event<span class="o">=</span><span 
class="nv">$set</span>,eType<span class="o">=</span>item,eId<span 
class="o">=</span>i42,tType<span class="o">=</span>None,tId<span 
class="o">=</span>None,p<span class="o">=</span>DataMap<span 
class="o">(</span>Map<span class="o">(</span>categories -&gt; JArray<span 
class="o">(</span>List<span class="o">(</span>JString<span 
class="o">(</span>c2<span class="o">)</span>, JString<span 
class="o">(</span>c1<span class="o">)</span>, JString<span 
class="o">(</span>c6<span class="o">)</span>, JString<span 
class="o">(</span>c3<span class="o">)))))</span>,t<span 
class="o">=</span>2015-05-15T21:31:19.349Z,tags<span 
class="o">=</span>List<span class="o">()</span>,pKey<span class="o">=
 </span>None,ct<span class="o">=</span>2015-05-15T21:31:19.354Z<span 
class="o">)</span>, Event<span class="o">(</span><span 
class="nv">id</span><span class="o">=</span>Some<span 
class="o">(</span>DjvP3Dnci9F4CWmiqoLabQAAAU1ZfROaqdRYO-pZ_no<span 
class="o">)</span>,event<span class="o">=</span><span 
class="nv">$set</span>,eType<span class="o">=</span>user,eId<span 
class="o">=</span>u9,tType<span class="o">=</span>None,tId<span 
class="o">=</span>None,p<span class="o">=</span>DataMap<span 
class="o">(</span>Map<span class="o">())</span>,t<span 
class="o">=</span>2015-05-15T21:31:18.810Z,tags<span 
class="o">=</span>List<span class="o">()</span>,pKey<span 
class="o">=</span>None,ct<span class="o">=</span>2015-05-15T21:31:18.817Z<span 
class="o">)</span>, Event<span class="o">(</span><span 
class="nv">id</span><span class="o">=</span>Some<span 
class="o">(</span>DjvP3Dnci9F4CWmiqoLabQAAAU1ZfRq7tsanlemwmZQ<span 
class="o">)</span>,event<span class="o">=</span>view,eType<span 
class="o">=</span>user,
 eId<span class="o">=</span>u9,tType<span class="o">=</span>Some<span 
class="o">(</span>item<span class="o">)</span>,tId<span 
class="o">=</span>Some<span class="o">(</span>i25<span 
class="o">)</span>,p<span class="o">=</span>DataMap<span 
class="o">(</span>Map<span class="o">())</span>,t<span 
class="o">=</span>2015-05-15T21:31:20.635Z,tags<span 
class="o">=</span>List<span class="o">()</span>,pKey<span 
class="o">=</span>None,ct<span class="o">=</span>2015-05-15T21:31:20.639Z<span 
class="o">)</span>, Event<span class="o">(</span><span 
class="nv">id</span><span class="o">=</span>Some<span 
class="o">(</span>DjvP3Dnci9F4CWmiqoLabQAAAU1ZfR...
+</pre></td></tr></tbody></table> </div> </div></div></div></div><footer><div 
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