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 >hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a 
 >href="#">Demo Tutorials</a><span class="spacer">&gt;</span></li><li><span 
 >class="last">Comics Recommendation Demo</span></li></ul></div><div 
 >id="page-title"><h1>Comics Recommendation Demo</h1></div></div><div 
 >id="table-of-c
 ontent-wrapper"><h5>On this page</h5><aside id="table-of-contents"><ul> <li> 
<a href="#introduction">Introduction</a> </li> <li> <a 
href="#tapster-demo-application">Tapster Demo Application</a> </li> <li> <a 
href="#apache-predictionio-setup">Apache PredictionIO Setup</a> </li> <li> <a 
href="#import-data">Import Data</a> </li> <li> <a 
href="#connect-demo-app-with-apache-predictionio">Connect Demo app with Apache 
PredictionIO</a> </li> <li> <a href="#links">Links</a> </li> <li> <a 
href="#conclusion">Conclusion</a> </li> </ul> </aside><hr/><a 
id="edit-page-link" 
href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/demo/tapster.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="#">Demo Tutorials</a><span 
class="spacer">&gt;</span></li><li><span class="last">Comics Recommendation 
Demo</span></li></ul></div><d
 iv id="page-title"><h1>Comics Recommendation Demo</h1></div></div><div 
class="content"> <h2 id='introduction' 
class='header-anchors'>Introduction</h2><p>In this demo, we will show you how 
to build a Tinder-style web application (named &quot;Tapster&quot;) 
recommending comics to users based on their likes/dislikes of episodes 
interactively.</p><p>The demo will use <a 
href="https://predictionio.apache.org/templates/similarproduct/quickstart/";>Similar
 Product Template</a>. Similar Product Template is a great choice if you want 
to make recommendations based on immediate user activities or for new users 
with limited history. It uses MLLib Alternating Least Squares (ALS) 
recommendation algorithm, a <a 
href="http://en.wikipedia.org/wiki/Recommender_system#Collaborative_filtering";>Collaborative
 filtering</a> (CF) algorithm commonly used for recommender systems. These 
techniques aim to fill in the missing entries of a user-item association 
matrix. Users and products are described by a small 
 set of latent factors that can be used to predict missing entries. A 
layman&#39;s interpretation of Collaborative Filtering is &quot;People who like 
this comic, also like these comics.&quot;</p><p>All the code and data is on 
GitHub at: <a 
href="https://github.com/PredictionIO/Demo-Tapster";>github.com/PredictionIO/Demo-Tapster</a>.</p><h3
 id='data' class='header-anchors'>Data</h3><p>The source of the data is from <a 
href="http://tapastic.com/";>Tapastic</a>. You can find the data files <a 
href="https://github.com/PredictionIO/Demo-Tapster/tree/master/data";>here</a>.</p><p>The
 data structure looks like this:</p><p><a 
href="https://github.com/PredictionIO/Demo-Tapster/blob/master/data/episode_list.csv";>Episode
 List</a> <code>data/episode_list.csv</code></p><p><strong>Fields:</strong> 
episodeId | episodeTitle | episodeCategories | episodeUrl | 
episodeImageUrls</p><p>1,000 rows. Each row represents one episode.</p><p><a 
href="https://github.com/PredictionIO/Demo-Tapster/blob/master/data/u
 ser_list.csv">User Like Event List</a> 
<code>data/user_list.csv</code></p><p><strong>Fields:</strong> userId | 
episodeId | likedTimestamp</p><p>192,587 rows. Each row represents one user 
like for the given episode.</p><p>The tutorial has four major steps:</p> <ul> 
<li>Demo application setup</li> <li>PredictionIO installation and setup</li> 
<li>Import data into database and PredictionIO</li> <li>Integrate demo 
application with PredictionIO</li> </ul> <h2 id='tapster-demo-application' 
class='header-anchors'>Tapster Demo Application</h2><p>The demo application is 
built using Rails.</p><p>You can clone the existing application with:</p><div 
class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
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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" 
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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/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 
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 >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 cl
 ass="level-3"><a class="final" 
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 an>Demo Tutorials</span></a><ul><li class="level-2"><a class="final active" 
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 pan>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" 
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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="#">Demo Tutorials</a><span class="spacer">&gt;</span></li><li><span 
class="last">Comics Recommendation Demo</span></li></ul></div><div 
id="page-title"><h1>Comics Recommendation Demo</h1></div></div><div id="table-o
 f-content-wrapper"><h5>On this page</h5><aside id="table-of-contents"><ul> 
<li> <a href="#introduction">Introduction</a> </li> <li> <a 
href="#tapster-demo-application">Tapster Demo Application</a> </li> <li> <a 
href="#apache-predictionio-setup">Apache PredictionIO Setup</a> </li> <li> <a 
href="#import-data">Import Data</a> </li> <li> <a 
href="#connect-demo-app-with-apache-predictionio">Connect Demo app with Apache 
PredictionIO</a> </li> <li> <a href="#links">Links</a> </li> <li> <a 
href="#conclusion">Conclusion</a> </li> </ul> </aside><hr/><a 
id="edit-page-link" 
href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/demo/tapster.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="#">Demo Tutorials</a><span 
class="spacer">&gt;</span></li><li><span class="last">Comics Recommendation 
Demo</span></li></ul></div
 ><div id="page-title"><h1>Comics Recommendation Demo</h1></div></div><div 
 >class="content"> <h2 id='introduction' 
 >class='header-anchors'>Introduction</h2><p>In this demo, we will show you how 
 >to build a Tinder-style web application (named &quot;Tapster&quot;) 
 >recommending comics to users based on their likes/dislikes of episodes 
 >interactively.</p><p>The demo will use <a 
 >href="https://predictionio.apache.org/templates/similarproduct/quickstart/";>Similar
 > Product Template</a>. Similar Product Template is a great choice if you want 
 >to make recommendations based on immediate user activities or for new users 
 >with limited history. It uses MLLib Alternating Least Squares (ALS) 
 >recommendation algorithm, a <a 
 >href="http://en.wikipedia.org/wiki/Recommender_system#Collaborative_filtering";>Collaborative
 > filtering</a> (CF) algorithm commonly used for recommender systems. These 
 >techniques aim to fill in the missing entries of a user-item association 
 >matrix. Users and products are described by a sma
 ll set of latent factors that can be used to predict missing entries. A 
layman&#39;s interpretation of Collaborative Filtering is &quot;People who like 
this comic, also like these comics.&quot;</p><p>All the code and data is on 
GitHub at: <a 
href="https://github.com/PredictionIO/Demo-Tapster";>github.com/PredictionIO/Demo-Tapster</a>.</p><h3
 id='data' class='header-anchors'>Data</h3><p>The source of the data is from <a 
href="http://tapastic.com/";>Tapastic</a>. You can find the data files <a 
href="https://github.com/PredictionIO/Demo-Tapster/tree/master/data";>here</a>.</p><p>The
 data structure looks like this:</p><p><a 
href="https://github.com/PredictionIO/Demo-Tapster/blob/master/data/episode_list.csv";>Episode
 List</a> <code>data/episode_list.csv</code></p><p><strong>Fields:</strong> 
episodeId | episodeTitle | episodeCategories | episodeUrl | 
episodeImageUrls</p><p>1,000 rows. Each row represents one episode.</p><p><a 
href="https://github.com/PredictionIO/Demo-Tapster/blob/master/dat
 a/user_list.csv">User Like Event List</a> 
<code>data/user_list.csv</code></p><p><strong>Fields:</strong> userId | 
episodeId | likedTimestamp</p><p>192,587 rows. Each row represents one user 
like for the given episode.</p><p>The tutorial has four major steps:</p> <ul> 
<li>Demo application setup</li> <li>PredictionIO installation and setup</li> 
<li>Import data into database and PredictionIO</li> <li>Integrate demo 
application with PredictionIO</li> </ul> <h2 id='tapster-demo-application' 
class='header-anchors'>Tapster Demo Application</h2><p>The demo application is 
built using Rails.</p><p>You can clone the existing application with:</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">$ </span>git clone  
https://github.com/PredictionIO/Demo-Tapster.git
 <span class="gp">$ </span><span class="nb">cd </span>Demo-Tapster
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-<!DOCTYPE html><html><head><title>Text Classification Engine 
Tutorial</title><meta charset="utf-8"/><meta content="IE=edge,chrome=1" 
http-equiv="X-UA-Compatible"/><meta name="viewport" 
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hidden-md hidden-lg"><img id="left-menu-indicator" 
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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="/batchpredict/"><span>Batch Predictions</span></a></li><li class="level-
 2"><a class="final" href="/deploy/monitoring/"><span>Monitoring 
Engine</span></a></li><li class="level-2"><a class="final" 
href="/deploy/engineparams/"><span>Setting Engine Parameters</span></a></li><li 
class="level-2"><a class="final" href="/deploy/enginevariants/"><span>Deploying 
Multiple Engine Variants</span></a></li><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" 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="expandib
 le" 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 
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="/eva
 luation/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="/templat
 es/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/"><spa
 n>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="#"><sp
 an>Classification</span></a><ul><li class="level-3"><a class="final" 
href="/templates/classification/quickstart/"><span>Quick 
Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/classification/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/classification/how-to/"><span>How-To</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/classification/add-algorithm/"><span>Use Alternative 
Algorithm</span></a></li><li class="level-3"><a class="final" 
href="/templates/classification/reading-custom-properties/"><span>Read Custom 
Properties</span></a></li></ul></li></ul></li><li class="level-1"><a 
class="expandible" href="#"><span>Engine Template Gallery</span></a><ul><li 
class="level-2"><a class="final" 
href="/gallery/template-gallery/"><span>Browse</span></a></li><li 
class="level-2"><a class="final" 
href="/community/submit-template/"><span>Submit your Engine as a 
Template</span></a></li></ul></li><li class="level-1"
 ><a class="expandible" href="#"><span>Demo Tutorials</span></a><ul><li 
 >class="level-2"><a class="final" href="/demo/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 active" 
 >href="/demo/textclassification/"><span>Text Classification Engine 
 >Tutorial</span></a></li></ul></li><li class="level-1"><a class="expandible" 
 >href="/community/"><span>Getting Involved</span></a><ul><li 
 >class="level-2"><a class="final" 
 >href="/community/contribute-code/"><span>Contribute Code</span></a></li><li 
 >class="level-2"><a class="final" 
 >href="/community/contribute-documentation/"><span>Contribute 
 >Documentation</span></a></li><li class="level-2"><a class="final" 
 >href="/community/contribute-sdk/"><span>Contribute a SDK</span></a></li><li 
 >class="level-2"><a class="final" 
 >href="/community/contribute-webhook/"><span>Contribute a 
 >Webhook</span></a></li><li class=
 "level-2"><a class="final" href="/community/projects/"><span>Community 
Projects</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><span>Getting Help</span></a><ul><li class="level-2"><a class="final" 
href="/resources/faq/"><span>FAQs</span></a></li><li class="level-2"><a 
class="final" href="/support/"><span>Support</span></a></li></ul></li><li 
class="level-1"><a class="expandible" 
href="#"><span>Resources</span></a><ul><li class="level-2"><a class="final" 
href="/cli/"><span>Command-line Interface</span></a></li><li class="level-2"><a 
class="final" href="/resources/release/"><span>Release 
Cadence</span></a></li><li class="level-2"><a class="final" 
href="/resources/intellij/"><span>Developing Engines with IntelliJ 
IDEA</span></a></li><li class="level-2"><a class="final" 
href="/resources/upgrade/"><span>Upgrade Instructions</span></a></li><li 
class="level-2"><a class="final" 
href="/resources/glossary/"><span>Glossary</span></a></li></ul></li><li 
class="level-1
 "><a class="expandible" href="#"><span>Apache Software 
Foundation</span></a><ul><li class="level-2"><a class="final" 
href="https://www.apache.org/";><span>Apache Homepage</span></a></li><li 
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href="https://www.apache.org/licenses/";><span>License</span></a></li><li 
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 class="level-2"><a class="final" 
href="https://www.apache.org/foundation/thanks.html";><span>Thanks</span></a></li><li
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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="#">Demo Tutorials</a><span class="spacer">&gt;</span></li><li><span 
class="last">Text Classification Engine Tutorial</span></li></ul></div><div 
id="page-title"><h1>Text Classifica
 tion Engine Tutorial</h1></div></div><div id="table-of-content-wrapper"><h5>On 
this page</h5><aside id="table-of-contents"><ul> <li> <a 
href="#introduction">Introduction</a> </li> <li> <a 
href="#prerequisites">Prerequisites</a> </li> <li> <a 
href="#engine-overview">Engine Overview</a> </li> <li> <a 
href="#quick-start">Quick Start</a> </li> </ul> </li> <li> <a 
href="#detailed-explanation-of-dase">Detailed Explanation of DASE</a> <ul> <li> 
<a href="#importing-data">Importing Data</a> </li> <li> <a 
href="#data-source-reading-event-data">Data Source: Reading Event Data</a> 
</li> <li> <a href="#preparator-data-processing-with-dase">Preparator : Data 
Processing With DASE</a> </li> <li> <a href="#algorithm-component">Algorithm 
Component</a> </li> <li> <a 
href="#serving-delivering-the-final-prediction">Serving: Delivering the Final 
Prediction</a> </li> <li> <a 
href="#evaluation-model-assessment-and-selection">Evaluation: Model Assessment 
and Selection</a> </li> <li> <a href="#engine-deploym
 ent">Engine Deployment</a> </li> </ul> </aside><hr/><a id="edit-page-link" 
href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/demo/textclassification.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="#">Demo Tutorials</a><span 
class="spacer">&gt;</span></li><li><span class="last">Text Classification 
Engine Tutorial</span></li></ul></div><div id="page-title"><h1>Text 
Classification Engine Tutorial</h1></div></div><div class="content"> 
<p>(Updated for Text Classification Template version 3.1)</p><h2 
id='introduction' class='header-anchors'>Introduction</h2><p>In the real world, 
there are many applications that collect text as data. For example, spam 
detectors take email and header content to automatically determine what is or 
is not spam; applications can gague the general sentiment in a geographica
 l area by analyzing Twitter data; and news articles can be automatically 
categorized based solely on the text content.There are a wide array of machine 
learning models you can use to create, or train, a predictive model to assign 
an incoming article, or query, to an existing category. Before you can use 
these techniques you must first transform the text data (in this case the set 
of news articles) into numeric vectors, or feature vectors, that can be used to 
train your model.</p><p>The purpose of this tutorial is to illustrate how you 
can go about doing this using PredictionIO&#39;s platform. The advantages of 
using this platform include: a dynamic engine that responds to queries in 
real-time; <a 
href="http://en.wikipedia.org/wiki/Separation_of_concerns";>separation of 
concerns</a>, which offers code re-use and maintainability, and distributed 
computing capabilities for scalability and efficiency. Moreover, it is easy to 
incorporate non-trivial data modeling tasks into the DASE archi
 tecture allowing Data Scientists to focus on tasks related to modeling. This 
tutorial will exemplify some of these ideas by guiding you through 
PredictionIO&#39;s <a 
href="/gallery/template-gallery/#natural-language-processing">text 
classification template</a>.</p><h2 id='prerequisites' 
class='header-anchors'>Prerequisites</h2><p>Before getting started, please make 
sure that you have the latest version of Apache PredictionIO <a 
href="http://predictionio.apache.org/install/";>installed</a>. We emphasize here 
that this is an engine template written in <strong>Scala</strong> and can be 
more generally thought of as an SBT project containing all the necessary 
components.</p><p>You should also download the engine template named Text 
Classification Engine that accompanies this tutorial by cloning the template 
repository:</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>git clone 
 >https://github.com/apache/incubator-predictionio-template-text-classifier.git 
 >&lt; Your new engine directory &gt;
+<!DOCTYPE html><html><head><title>Text Classification Engine 
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 ><div class="row"><div id="left-menu-wrapper" class="col-md-3"><nav 
 >id="nav-main"><ul><li class="level-1"><a class="expandible" 
 >href="/"><span>Apache PredictionIO® 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 You
 r App</span></a><ul><li class="level-2"><a class="final" 
href="/appintegration/"><span>App Integration Overview</span></a></li><li 
class="level-2"><a class="expandible" href="/sdk/"><span>List of 
SDKs</span></a><ul><li class="level-3"><a class="final" 
href="/sdk/java/"><span>Java & Android SDK</span></a></li><li 
class="level-3"><a class="final" href="/sdk/php/"><span>PHP 
SDK</span></a></li><li class="level-3"><a class="final" 
href="/sdk/python/"><span>Python SDK</span></a></li><li class="level-3"><a 
class="final" href="/sdk/ruby/"><span>Ruby SDK</span></a></li><li 
class="level-3"><a class="final" href="/sdk/community/"><span>Community Powered 
SDKs</span></a></li></ul></li></ul></li><li class="level-1"><a 
class="expandible" href="#"><span>Deploying an Engine</span></a><ul><li 
class="level-2"><a class="final" href="/deploy/"><span>Deploying as a Web 
Service</span></a></li><li class="level-2"><a class="final" 
href="/batchpredict/"><span>Batch Predictions</span></a></li><li class="level
 -2"><a class="final" href="/deploy/monitoring/"><span>Monitoring 
Engine</span></a></li><li class="level-2"><a class="final" 
href="/deploy/engineparams/"><span>Setting Engine Parameters</span></a></li><li 
class="level-2"><a class="final" href="/deploy/enginevariants/"><span>Deploying 
Multiple Engine Variants</span></a></li><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" 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="expandi
 ble" 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 
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="/ev
 aluation/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 
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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" 
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Properties</span></a></li><li class="level-3"><a class="final" 
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Event</span></a></li><li class="level-3"><a class="final" 
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for Users</span></a></li><li class="level-3"><a class="final" 
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href="#">
 <span>Classification</span></a><ul><li class="level-3"><a class="final" 
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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 
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 -1"><a class="expandible" href="#"><span>Demo Tutorials</span></a><ul><li 
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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="#">Demo Tutorials</a><span class="spacer">&gt;</span></li><li><span 
class="last">Text Classification Engine Tutorial</span></li></ul></div><div 
id="page-title"><h1>Text Classif
 ication Engine Tutorial</h1></div></div><div 
id="table-of-content-wrapper"><h5>On this page</h5><aside 
id="table-of-contents"><ul> <li> <a href="#introduction">Introduction</a> </li> 
<li> <a href="#prerequisites">Prerequisites</a> </li> <li> <a 
href="#engine-overview">Engine Overview</a> </li> <li> <a 
href="#quick-start">Quick Start</a> </li> </ul> </li> <li> <a 
href="#detailed-explanation-of-dase">Detailed Explanation of DASE</a> <ul> <li> 
<a href="#importing-data">Importing Data</a> </li> <li> <a 
href="#data-source-reading-event-data">Data Source: Reading Event Data</a> 
</li> <li> <a href="#preparator-data-processing-with-dase">Preparator : Data 
Processing With DASE</a> </li> <li> <a href="#algorithm-component">Algorithm 
Component</a> </li> <li> <a 
href="#serving-delivering-the-final-prediction">Serving: Delivering the Final 
Prediction</a> </li> <li> <a 
href="#evaluation-model-assessment-and-selection">Evaluation: Model Assessment 
and Selection</a> </li> <li> <a href="#engine-depl
 oyment">Engine Deployment</a> </li> </ul> </aside><hr/><a id="edit-page-link" 
href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/demo/textclassification.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="#">Demo Tutorials</a><span 
class="spacer">&gt;</span></li><li><span class="last">Text Classification 
Engine Tutorial</span></li></ul></div><div id="page-title"><h1>Text 
Classification Engine Tutorial</h1></div></div><div class="content"> 
<p>(Updated for Text Classification Template version 3.1)</p><h2 
id='introduction' class='header-anchors'>Introduction</h2><p>In the real world, 
there are many applications that collect text as data. For example, spam 
detectors take email and header content to automatically determine what is or 
is not spam; applications can gague the general sentiment in a geograph
 ical area by analyzing Twitter data; and news articles can be automatically 
categorized based solely on the text content.There are a wide array of machine 
learning models you can use to create, or train, a predictive model to assign 
an incoming article, or query, to an existing category. Before you can use 
these techniques you must first transform the text data (in this case the set 
of news articles) into numeric vectors, or feature vectors, that can be used to 
train your model.</p><p>The purpose of this tutorial is to illustrate how you 
can go about doing this using PredictionIO&#39;s platform. The advantages of 
using this platform include: a dynamic engine that responds to queries in 
real-time; <a 
href="http://en.wikipedia.org/wiki/Separation_of_concerns";>separation of 
concerns</a>, which offers code re-use and maintainability, and distributed 
computing capabilities for scalability and efficiency. Moreover, it is easy to 
incorporate non-trivial data modeling tasks into the DASE ar
 chitecture allowing Data Scientists to focus on tasks related to modeling. 
This tutorial will exemplify some of these ideas by guiding you through 
PredictionIO&#39;s <a 
href="/gallery/template-gallery/#natural-language-processing">text 
classification template</a>.</p><h2 id='prerequisites' 
class='header-anchors'>Prerequisites</h2><p>Before getting started, please make 
sure that you have the latest version of Apache PredictionIO <a 
href="http://predictionio.apache.org/install/";>installed</a>. We emphasize here 
that this is an engine template written in <strong>Scala</strong> and can be 
more generally thought of as an SBT project containing all the necessary 
components.</p><p>You should also download the engine template named Text 
Classification Engine that accompanies this tutorial by cloning the template 
repository:</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="co
 de"><pre>git clone 
https://github.com/apache/incubator-predictionio-template-text-classifier.git 
&lt; Your new engine directory &gt;
 </pre></td></tr></tbody></table> </div> <h2 id='engine-overview' 
class='header-anchors'>Engine Overview</h2><p>The engine follows the DASE 
architecture which we briefly review here. As a user, you are tasked with 
collecting data for your web or application, and importing it into 
PredictionIO&#39;s Event Server. Once the data is in the server, it can be read 
and processed by the engine via the Data Source and Preparation components, 
respectively. The Algorithm component uses the processed, or prepared, data to 
train a set of predictive models. Once you have trained these models, you are 
ready to deploy your engine and respond to real-time queries via the Serving 
component which combines the results from different fitted models. The 
Evaluation component is used to compute an appropriate metric to test the 
performance of a fitted model, as well as aid in the tuning of model hyper 
parameters.</p><p>This engine template is meant to handle text classification 
which means you will be worki
 ng with text data. This means that a query, or newly observed documents, will 
be of the form:</p><p><code>{text : String}</code>.</p><p>In the running 
example, a query would be an incoming news article. Once the engine is deployed 
it can process the query, and then return a Predicted Result of the 
form</p><p><code>{category : String, confidence : Double}</code>.</p><p>Here 
category is the model&#39;s class assignment for this new text document (i.e. 
the best guess for this article&#39;s categorization), and confidence, a value 
between 0 and 1 representing your confidence in the category prediction (0 
meaning you have no confidence in the prediction). The Actual Result is of the 
form</p><p><code>{category : String}</code>.</p><p>This is used in the 
evaluation stage when estimating the performance of your predictive model (how 
well does the model predict categories). Please refer to the <a 
href="https://predictionio.apache.org/customize/";>following tutorial</a> for a 
more detailed exp
 lanation of how your engine will interact with your web application, as well 
as an in depth-overview of DASE.</p><h2 id='quick-start' 
class='header-anchors'>Quick Start</h2><p>This is a quick start guide in case 
you want to start using the engine right away. Sample email data for spam 
classification will be used. For more detailed information, read the subsequent 
sections.</p><h3 id='1.-create-a-new-application.' class='header-anchors'>1. 
Create a new application.</h3><p>After the application is created, you will be 
given an access key and application ID for the application.</p><div 
class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td 
class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td 
class="code"><pre><span class="gp">$ </span>pio app new MyTextApp
 </pre></td></tr></tbody></table> </div> <h3 id='2.-import-the-tutorial-data.' 
class='header-anchors'>2. Import the tutorial data.</h3><p>There are three 
different data sets available, each giving a different use case for this 
engine. Please refer to the <strong>Data Source: Reading Event Data</strong> 
section to see how to appropriate modify the <code>DataSource</code> class for 
use with each respective data set. The default data set is an e-mail spam data 
set.</p><p>These data sets have already been processed and are ready for <a 
href="/datacollection/batchimport/">batch import</a>. Replace <code>***</code> 
with your actual application ID.</p><div class="highlight shell"><table 
style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: 
right"><pre class="lineno">1
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