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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" 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://predictionio.apache.org/demo/textclassification/"/><link href="/images/favicon/normal-b330020a.png" rel="shortcut icon"/><link href="/images/favicon/apple-c0febcf2.png" rel="apple-touch-icon"/><link href="//fonts.googleapis.com/css?family=Open+Sans:300italic,400italic,600italic,700italic,800italic,400,300,600,700,800" rel="stylesheet"/><link href="//maxcdn.bootstrapcdn.com/font-awesome/4.2.0/css/font-awesome.min.css" rel="stylesheet"/><link href="/stylesheets/application-eccfc6cb.css" rel="stylesheet" type="text/css"/><script src="//cdnjs.cloudflare.com/ajax/libs/html5shiv/3.7.2/html5shiv.min.js"></script><script src=
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 l-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 col-sm-12 col-xs-12 swiftype-wrapper"><div class="swiftype"><form class="search-form"><img class="search-box-toggler hidden-xs hidden-sm" src="/images/icons/search-glass-704bd4ff.png"/><div class="search-box"><img src="/images/icons/search-glass-704bd4ff.png"/><input type="text" id="st-search-input" class="st-search-input" placeholder="Search Doc..."/></div><img class="swiftype-row-hider hidden-md hidden-lg" src="/images/icons/drawer-toggle-active-fcbef12a.png"/></form></div></div><div class="mobile-left-menu-toggler hidden-md hidden-lg"></div></div></div></div><div id="page" class="container-fluid">
 <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 Your
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 "><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="/templ
 ates/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/"><s
 pan>Quick Start</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/dase/"><span>DASE</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/how-to/"><span>How-To</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/multi-events-multi-algos/"><span>Multiple Events and Multiple Algorithms</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/return-item-properties/"><span>Returns Item Properties</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/train-with-rate-event/"><span>Train with Rate Event</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/rid-user-set-event/"><span>Get Rid of Events for Users</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/recommended-user/"><span>Recommend Users</span></a></li></ul></li><li class="level-2"><a class="expandible" href="#"><
 span>Classification</span></a><ul><li class="level-3"><a 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 clas
 s="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 class="level-2"><a class="final" href="https://www.apache.org/licenses/"><span>License</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/foundation/sponsorship.html"><span>Sponsorship</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/foundation/thanks.html"><span>Thanks</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/security/"><span>Security</span></a></li></ul></li></ul></nav></div><div class="col-md-9 col-sm-12"><div class="content-header hidden-md hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a href="#">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 Classifi
 cation 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-deplo
 yment">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 geographi
 cal 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 arc
 hitecture 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="cod
 e"><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 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://predictionio.apache.org/demo/textclassification/"/><link href="/images/favicon/normal-b330020a.png" rel="shortcut icon"/><link href="/images/favicon/apple-c0febcf2.png" rel="apple-touch-icon"/><link href="//fonts.googleapis.com/css?family=Open+Sans:300italic,400italic,600italic,700italic,800italic,400,300,600,700,800" rel="stylesheet"/><link href="//maxcdn.bootstrapcdn.com/font-awesome/4.2.0/css/font-awesome.min.css" rel="stylesheet"/><link href="/stylesheets/application-eccfc6cb.css" rel="stylesheet" type="text/css"/><script src="//cdnjs.cloudflare.com/ajax/libs/html5shiv/3.7.2/html5shiv.min.js"></script><script src=
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 -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 col-sm-12 col-xs-12 swiftype-wrapper"><div class="swiftype"><form class="search-form"><img class="search-box-toggler hidden-xs hidden-sm" src="/images/icons/search-glass-704bd4ff.png"/><div class="search-box"><img src="/images/icons/search-glass-704bd4ff.png"/><input type="text" id="st-search-input" class="st-search-input" placeholder="Search Doc..."/></div><img class="swiftype-row-hider hidden-md hidden-lg" src="/images/icons/drawer-toggle-active-fcbef12a.png"/></form></div></div><div class="mobile-left-menu-toggler hidden-md hidden-lg"></div></div></div></div><div id="page" class="container-fluid"><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 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 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 clas
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 tricchoose/"><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
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 mendation/batch-evaluator/"><span>Batch Persistable Evaluator</span></a></li></ul></li><li class="level-2"><a class="expandible" href="#"><span>E-Commerce Recommendation</span></a><ul><li class="level-3"><a class="final" href="/templates/ecommercerecommendation/quickstart/"><span>Quick Start</span></a></li><li class="level-3"><a class="final" href="/templates/ecommercerecommendation/dase/"><span>DASE</span></a></li><li class="level-3"><a class="final" href="/templates/ecommercerecommendation/how-to/"><span>How-To</span></a></li><li class="level-3"><a class="final" href="/templates/ecommercerecommendation/train-with-rate-event/"><span>Train with Rate Event</span></a></li><li class="level-3"><a class="final" href="/templates/ecommercerecommendation/adjust-score/"><span>Adjust Score</span></a></li></ul></li><li class="level-2"><a class="expandible" href="#"><span>Similar Product</span></a><ul><li class="level-3"><a class="final" href="/templates/similarproduct/quickstart/"><span>Quick 
 Start</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/dase/"><span>DASE</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/how-to/"><span>How-To</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/multi-events-multi-algos/"><span>Multiple Events and Multiple Algorithms</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/return-item-properties/"><span>Returns Item Properties</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/train-with-rate-event/"><span>Train with Rate Event</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/rid-user-set-event/"><span>Get Rid of Events for Users</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/recommended-user/"><span>Recommend Users</span></a></li></ul></li><li class="level-2"><a class="expandible" href="#"><span>Class
 ification</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 clas
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 ss="expandible" href="#"><span>Apache Software Foundation</span></a><ul><li class="level-2"><a class="final" href="https://www.apache.org/"><span>Apache Homepage</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/licenses/"><span>License</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/foundation/sponsorship.html"><span>Sponsorship</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/foundation/thanks.html"><span>Thanks</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/security/"><span>Security</span></a></li></ul></li></ul></nav></div><div class="col-md-9 col-sm-12"><div class="content-header hidden-md hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a href="#">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 Eng
 ine 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-deployment">Eng
 ine Deployment</a> </li> </ul> </aside><hr/><a id="edit-page-link" href="https://github.com/apache/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 analyzin
 g 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 D
 ata 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 ht
 tps://github.com/apache/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
 2
@@ -33,7 +33,7 @@
 26</pre></td><td class="code"><pre><span class="o">{</span>
   <span class="s2">"id"</span>: <span class="s2">"default"</span>,
   <span class="s2">"description"</span>: <span class="s2">"Default settings"</span>,
-  <span class="s2">"engineFactory"</span>: <span class="s2">"org.template.textclassification.TextClassificationEngine"</span>,
+  <span class="s2">"engineFactory"</span>: <span class="s2">"org.example.textclassification.TextClassificationEngine"</span>,
   <span class="s2">"datasource"</span>: <span class="o">{</span>
     <span class="s2">"params"</span>: <span class="o">{</span>
       <span class="s2">"appName"</span>: <span class="s2">"MyTextApp"</span>
@@ -70,7 +70,7 @@
 </pre></td></tr></tbody></table> </div> <p>you should see following outputs returned by the engine:</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="o">{</span><span class="s2">"category"</span>:<span class="s2">"not spam"</span>,<span class="s2">"confidence"</span>:0.852619510921587<span class="o">}</span>
 </pre></td></tr></tbody></table> </div> <p>Try another query:</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>curl -H <span class="s2">"Content-Type: application/json"</span> -d <span class="s1">'{ "text":"Earn extra cash!" }'</span> http://localhost:8000/queries.json
 </pre></td></tr></tbody></table> </div> <p>you should see following outputs returned by the engine:</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="o">{</span><span class="s2">"category"</span>:<span class="s2">"spam"</span>,<span class="s2">"confidence"</span>:0.5268770133242983<span class="o">}</span>
-</pre></td></tr></tbody></table> </div> <h3 id='5.b.evaluate-your-training-model-and-tune-parameters.' class='header-anchors'>5.b.Evaluate your training model and tune parameters.</h3><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 <span class="nb">eval </span>org.template.textclassification.AccuracyEvaluation org.template.textclassification.EngineParamsList
+</pre></td></tr></tbody></table> </div> <h3 id='5.b.evaluate-your-training-model-and-tune-parameters.' class='header-anchors'>5.b.Evaluate your training model and tune parameters.</h3><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 <span class="nb">eval </span>org.example.textclassification.AccuracyEvaluation org.example.textclassification.EngineParamsList
 </pre></td></tr></tbody></table> </div> <p><strong>Note:</strong> Training and evaluation stages are generally different stages of engine development. Evaluation is there to help you choose the best <a href="/evaluation/paramtuning/">algorithm parameters</a> to use for training an engine that is to be deployed as a web service.</p><p>Depending on your needs, in steps (5.x.) above, you can configure your Spark settings by typing a command of the form:</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 <span class="nb">command </span>command_parameters -- --master url --driver-memory <span class="o">{</span>0<span class="o">}</span>G --executor-memory <span class="o">{</span>1<span class="o">}</span>G --conf spark.akka.framesize<span class="o">={</span>2<span class="o">}</span> --total_executor_cores <span class="o">{</span>3<span
  class="o">}</span>
 </pre></td></tr></tbody></table> </div> <p>Only the latter commands are listed as these are some of the more commonly modified values. See the <a href="https://spark.apache.org/docs/latest/spark-standalone.html">Spark documentation</a> and the <a href="http://predictionio.apache.org/resources/faq/">PredictionIO FAQ&#39;s</a> for more information.</p><p><strong>Note:</strong> We recommend you set your driver memory to <code>1G</code> or <code>2G</code> as the data size when dealing with text can be very large.</p><h1 id='detailed-explanation-of-dase' class='header-anchors'>Detailed Explanation of DASE</h1><h2 id='importing-data' class='header-anchors'>Importing Data</h2><p>In the quick start, email spam classification is used. This template can easily be modified for other types text classification.</p><p>If you want to import different sets of data, follow the Quick Start instructions to import data from different files. Make sure that the Data Source is modified accordingly to matc
 h the <code>event</code>, <code>entityType</code>, and <code>properties</code> fields set for the specific dataset. The following section explains this in more detail.</p><h2 id='data-source:-reading-event-data' class='header-anchors'>Data Source: Reading Event Data</h2><p>Now that the data has been imported into PredictionIO&#39;s Event Server, it needs to be read from storage to be used by the engine. This is precisely what the DataSource engine component is for, which is implemented in the template script <code>DataSource.scala</code>. The class <code>Observation</code> serves as a wrapper for storing the information about a news document needed to train a model. The attribute label refers to the label of the category a document belongs to, and text, stores the actual document content as a string. The class TrainingData is used to store an RDD of Observation objects along with the set of stop words.</p><p>The class <code>DataSourceParams</code> is used to specify the parameters n
 eeded to read and prepare the data for processing. This class is initialized with two parameters <code>appName</code> and <code>evalK</code>. The first parameter specifies your application name (i.e. MyTextApp), which is needed so that the DataSource component knows where to pull the event data from. The second parameter is used for model evaluation and specifies the number of folds to use in <a href="http://en.wikipedia.org/wiki/Cross-validation_%28statistics%29">cross-validation</a> when estimating a model performance metric.</p><p>The final and most important ingredient is the DataSource class. This is initialized with its corresponding parameter class, and extends <code>PDataSource</code>. This <strong>must</strong> implement the method <code>readTraining</code> which returns an instance of type TrainingData. This method completely relies on the defined private methods readEventData and readStopWords. Both of these functions read data observations as Event instances, create an R
 DD containing these events and finally transforms the RDD of events into an object of the appropriate type as seen below:</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
@@ -493,7 +493,7 @@
 23</pre></td><td class="code"><pre><span class="w">  </span><span class="p">{</span><span class="w">
   </span><span class="s2">"id"</span><span class="p">:</span><span class="w"> </span><span class="s2">"default"</span><span class="p">,</span><span class="w">
   </span><span class="s2">"description"</span><span class="p">:</span><span class="w"> </span><span class="s2">"Default settings"</span><span class="p">,</span><span class="w">
-  </span><span class="s2">"engineFactory"</span><span class="p">:</span><span class="w"> </span><span class="s2">"org.template.textclassification.TextClassificationEngine"</span><span class="p">,</span><span class="w">
+  </span><span class="s2">"engineFactory"</span><span class="p">:</span><span class="w"> </span><span class="s2">"org.example.textclassification.TextClassificationEngine"</span><span class="p">,</span><span class="w">
   </span><span class="s2">"datasource"</span><span class="p">:</span><span class="w"> </span><span class="p">{</span><span class="w">
     </span><span class="s2">"params"</span><span class="p">:</span><span class="w"> </span><span class="p">{</span><span class="w">
       </span><span class="s2">"appName"</span><span class="p">:</span><span class="w"> </span><span class="s2">"MyTextApp"</span><span class="w">
@@ -543,11 +543,11 @@
 </pre></td></tr></tbody></table> </div> <h2 id='engine-deployment' class='header-anchors'>Engine Deployment</h2><p>Once an engine is ready for deployment it can interact with your web application in real-time. This section will cover how to send and receive queries from your engine, gather more data, and re-training your model with the newly gathered data.</p><h3 id='sending-queries' class='header-anchors'>Sending Queries</h3><p>Recall that one of the greatest advantages of using the PredictionIO platform is that once your engine is deployed, you can respond to queries in real-time. Recall that our queries are of the form</p><p><code>{&quot;text&quot; : &quot;...&quot;}</code>.</p><p>To actually send a query you can use our REST API by typing in the following shell command:</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>curl -H <span class="s2">"Content-
 Type: application/json"</span> -d <span class="s1">'{ "text":"I like speed and fast motorcycles." }'</span> http://localhost:8000/queries.json
 </pre></td></tr></tbody></table> </div> <p>There are a number of <a href="https://github.com/PredictionIO">SDK&#39;s</a> you can use to send your queries and obtain a response. Recall that our predicted response is of the form</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="o">{</span><span class="s2">"category"</span> : <span class="s2">"class"</span>, <span class="s2">"confidence"</span> : 1.0<span class="o">}</span>
 </pre></td></tr></tbody></table> </div> <p>which is what you should see upon inputting the latter command for querying.</p><h3 id='gathering-more-data-and-retraining-your-model' class='header-anchors'>Gathering More Data and Retraining Your Model</h3><p>The importing data section that is included in this tutorial uses a sample data set for illustration purposes, and uses the PredictionIO Python SDK to import the data. However, there are a variety of ways that you can <a href="//predictionio.apache.org/datacollection/eventapi/">import</a> your collected data (via REST or other SDKs).</p><p>As you continue to collect your data, it is quite easy to retrain your model once you actually import your data into the Event Server. You simply repeat the steps listed in the Quick Start guide. We re-list them here again:</p><p><strong>1.</strong> Build your engine.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre cla
 ss="lineno">1</pre></td><td class="code"><pre><span class="gp">$ </span>pio build
-</pre></td></tr></tbody></table> </div> <p><strong>2.a.</strong> Evaluate your training model and tune parameters.</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 <span class="nb">eval </span>org.template.textclassification.AccuracyEvaluation org.template.textclassification.EngineParamsList
+</pre></td></tr></tbody></table> </div> <p><strong>2.a.</strong> Evaluate your training model and tune parameters.</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 <span class="nb">eval </span>org.example.textclassification.AccuracyEvaluation org.example.textclassification.EngineParamsList
 </pre></td></tr></tbody></table> </div> <p><strong>2.b.</strong> Train your model and deploy.</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><span class="gp">$ </span>pio train
 <span class="gp">$ </span>pio deploy
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