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-<!DOCTYPE html><html><head><title>Text Classification Engine 
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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 
id="tab
 le-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-deployment">Engine Deployment</a> 
</li> </ul> </aside><h
 r/><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 geographical area by 
analyzing Twitter data; and news article
 s 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 architecture 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 [text classification template(<a 
href="http://templates.prediction.io/PredictionIO/template-scala-parallel-textclassification/";>http://templates.prediction.io/PredictionIO/template-scala-parallel-textclassification/</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 (incubating) <a 
href="http://predictionio.incubator.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>pio template get 
PredictionIO/template-scala-parallel-textclassification &lt; Your new engine 
directory &gt;
+<!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" 
content="width=device-width, initial-scale=1.0"/><meta class="swiftype" 
name="title" data-type="string" content="Text Classification Engine 
Tutorial"/><link rel="canonical" 
href="https://docs.prediction.io/demo/textclassification/"/><link 
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class="container" id="header-wrapper"><div class="row"><div 
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src="/images/logos/logo-ee2b9bb3.png"/></a></div><div id="menu-wrapper"><div 
id="pill-wrapper"><a class="pill left" 
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SOURCE</a></div></div><img class="mobile-search-bar-toggler hidden-md 
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id="search-bar-row"><div class="row"><div class="col-md-9 col-sm-11 
 col-xs-11"><div class="hidden-md hidden-lg" 
id="mobile-page-heading-wrapper"><p>PredictionIO Docs</p><h4>Text 
Classification Engine Tutorial</h4></div><h4 class="hidden-sm 
hidden-xs">PredictionIO Docs</h4></div><div class="col-md-3 col-sm-1 col-xs-1 
hidden-md hidden-lg"><img id="left-menu-indicator" 
src="/images/icons/down-arrow-dfe9f7fe.png"/></div><div class="col-md-3 
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src="/images/icons/search-glass-704bd4ff.png"/><div class="search-box"><img 
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id="st-search-input" class="st-search-input" placeholder="Search 
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 <div id="left-menu-wrapper" class="col-md-3"><nav id="nav-main"><ul><li 
class="level-1"><a class="expandible" href="/"><span>Apache PredictionIO 
(incubating) Documentation</span></a><ul><li class="level-2"><a class="final" 
href="/"><span>Welcome to Apache PredictionIO 
(incubating)</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><span>Getting Started</span></a><ul><li class="level-2"><a 
class="final" href="/start/"><span>A Quick Intro</span></a></li><li 
class="level-2"><a class="final" href="/install/"><span>Installing Apache 
PredictionIO (incubating)</span></a></li><li class="level-2"><a class="final" 
href="/start/download/"><span>Downloading an Engine Template</span></a></li><li 
class="level-2"><a class="final" href="/start/deploy/"><span>Deploying Your 
First Engine</span></a></li><li class="level-2"><a class="final" 
href="/start/customize/"><span>Customizing the 
Engine</span></a></li></ul></li><li class="level-1"><a class="expandible" 
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 egrating with Your App</span></a><ul><li class="level-2"><a class="final" 
href="/appintegration/"><span>App Integration Overview</span></a></li><li 
class="level-2"><a class="expandible" href="/sdk/"><span>List of 
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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="/cli/#engine-commands"><span>Engine Command-line Inter
 face</span></a></li><li class="level-2"><a class="final" 
href="/deploy/monitoring/"><span>Monitoring Engine</span></a></li><li 
class="level-2"><a class="final" href="/deploy/engineparams/"><span>Setting 
Engine Parameters</span></a></li><li class="level-2"><a class="final" 
href="/deploy/enginevariants/"><span>Deploying Multiple Engine 
Variants</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><span>Customizing an Engine</span></a><ul><li class="level-2"><a 
class="final" href="/customize/"><span>Learning DASE</span></a></li><li 
class="level-2"><a class="final" href="/customize/dase/"><span>Implement 
DASE</span></a></li><li class="level-2"><a class="final" 
href="/customize/troubleshooting/"><span>Troubleshooting Engine 
Development</span></a></li><li class="level-2"><a class="final" 
href="/api/current/#package"><span>Engine Scala 
APIs</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><span>Collecting and Analyzing Data</span></a><ul><l
 i 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>Choosi
 ng an Algorithm(s)</span></a><ul><li class="level-2"><a class="final" 
href="/algorithm/"><span>Built-in Algorithm Libraries</span></a></li><li 
class="level-2"><a class="final" href="/algorithm/switch/"><span>Switching to 
Another Algorithm</span></a></li><li class="level-2"><a class="final" 
href="/algorithm/multiple/"><span>Combining Multiple 
Algorithms</span></a></li><li class="level-2"><a class="final" 
href="/algorithm/custom/"><span>Adding Your Own 
Algorithms</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><span>ML Tuning and Evaluation</span></a><ul><li class="level-2"><a 
class="final" href="/evaluation/"><span>Overview</span></a></li><li 
class="level-2"><a class="final" 
href="/evaluation/paramtuning/"><span>Hyperparameter Tuning</span></a></li><li 
class="level-2"><a class="final" 
href="/evaluation/evaluationdashboard/"><span>Evaluation 
Dashboard</span></a></li><li class="level-2"><a class="final" 
href="/evaluation/metricchoose/"><span>Choosing Evaluat
 ion 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>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 cl
 ass="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 
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href="/community/projects/"><span>Community 
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href="#"><span>Getting Help</span
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 >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 
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 >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="#">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 
 >id="tabl
 e-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-deployment">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 geographical 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 architecture 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 (incubating) <a 
href="http://predictionio.incubator.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>pio template get 
Predi
 ctionIO/template-scala-parallel-textclassification &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://docs.prediction.io/customize/";>following tutorial</a> for a more 
detailed explanat
 ion 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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 /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" 
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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" 
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 >class="final" href="/system/"><span>Architecture Overview</span></a></li><li 
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 >Another Data Store</span></a></li></ul></li><li class="level-1"><a 
 >class="expandible" href="#"><span>Engine Template Gallery</span></a><ul><li 
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 >Template</span></a></li></ul></li><li class="level-1"><a class="expandible" 
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href="#">Deploying an Engine</a><span class="spacer">&gt;</span></li><li><span 
class="last">Setting Engine Parameters</span></li></ul></div><div 
id="page-title"><h1>Setting Engine Parameters</h1></div></div><div 
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