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href="#">Demo Tutorials</a><span class="spacer">></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
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class="hidden-sm hidden xs"><ul><li><a href="#">Demo Tutorials</a><span
class="spacer">></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'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'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
< Your new engine directory >
+<!DOCTYPE html><html><head><title>Text Classification Engine
Tutorial</title><meta charset="utf-8"/><meta content="IE=edge,chrome=1"
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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
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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="/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"
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class="final" href="/community/contribute-code/"><span>Contribute
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IDEA</span></a></li><li class="level-2"><a class="final"
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-1"><a class="expandible" href="#"><span>Apache Software
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href="https://www.apache.org/";><span>Apache Homepage</span></a></li><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">></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">></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'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'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
< Your new engine directory >
</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'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's class assignment for this new text document (i.e.
the best guess for this article'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
@@ -547,7 +547,7 @@
</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
-</pre></td></tr></tbody></table> </div> </div></div></div></div><footer><div
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