{"id":916,"date":"2012-02-16T01:45:00","date_gmt":"2012-02-16T01:45:00","guid":{"rendered":"http:\/\/www.javacodegeeks.com\/2012\/10\/apache-mahout-getting-started.html"},"modified":"2012-10-21T23:00:31","modified_gmt":"2012-10-21T23:00:31","slug":"apache-mahout-getting-started","status":"publish","type":"post","link":"https:\/\/www.javacodegeeks.com\/2012\/02\/apache-mahout-getting-started.html","title":{"rendered":"Apache Mahout: Getting started"},"content":{"rendered":"
Recently I have got an interesting problem to solve: how to classify text from different sources using automation? Some time ago I read about a project which does this as well as many other text analysis stuff – Apache Mahout<\/a>. Though it’s not a very mature one (current version is 0.4<\/a>), it’s very powerful and scalable. Build on top of another excellent project, Apache Hadoop<\/a>, it’s capable to analyze huge data sets.<\/p>\n

So I did a small project in order to understand how Apache Mahout<\/a> works. I decided to use Apache Maven 2<\/a> in order to manage all dependencies so I will start with POM file first.<\/p>\n

<!--?xml version=\"1.0\" encoding=\"UTF-8\"?-->\r\n<project xmlns=\"http:\/\/maven.apache.org\/POM\/4.0.0\" xmlns:xsi=\"http:\/\/www.w3.org\/2001\/XMLSchema-instance\" xsi:schemalocation=\"http:\/\/maven.apache.org\/POM\/4.0.0 http:\/\/maven.apache.org\/maven-v4_0_0.xsd\">\r\n  <modelversion>4.0.0<\/modelversion>\r\n  <groupid>org.acme<\/groupid>\r\n  <artifactid>mahout<\/artifactid>\r\n  <version>0.94<\/version>\r\n  <name>Mahout Examples<\/name>\r\n  <description>Scalable machine learning library examples<\/description>\r\n  <packaging>jar<\/packaging>\r\n\r\n  <properties>\r\n    <project.build.sourceencoding>UTF-8<\/project.build.sourceencoding>\r\n    <apache.mahout.version>0.4<\/apache.mahout.version>\r\n  <\/properties>\r\n \r\n  <build>\r\n    <plugins>\r\n      <plugin>\r\n        <groupid>org.apache.maven.plugins<\/groupid>\r\n        <artifactid>maven-compiler-plugin<\/artifactid>\r\n        <configuration>\r\n          <encoding>UTF-8<\/encoding>\r\n          <source>1.6\r\n          <target>1.6<\/target>\r\n          <optimize>true<\/optimize>\r\n        <\/configuration>\r\n      <\/plugin>\r\n    <\/plugins>\r\n  <\/build>\r\n\r\n  <dependencies>\r\n    <dependency>\r\n      <groupid>org.apache.mahout<\/groupid>\r\n      <artifactid>mahout-core<\/artifactid>\r\n      <version>${apache.mahout.version}<\/version>\r\n    <\/dependency>\r\n\r\n    <dependency>\r\n      <groupid>org.apache.mahout<\/groupid>\r\n      <artifactid>mahout-math<\/artifactid>\r\n      <version>${apache.mahout.version}<\/version>\r\n    <\/dependency>\r\n\r\n    <dependency>\r\n      <groupid>org.apache.mahout<\/groupid>\r\n      <artifactid>mahout-utils<\/artifactid>\r\n      <version>${apache.mahout.version}<\/version>\r\n    <\/dependency>\r\n\r\n\r\n     <dependency>\r\n      <groupid>org.slf4j<\/groupid>\r\n      <artifactid>slf4j-api<\/artifactid>\r\n      <version>1.6.0<\/version>\r\n    <\/dependency>\r\n\r\n    <dependency>\r\n      <groupid>org.slf4j<\/groupid>\r\n      <artifactid>slf4j-jcl<\/artifactid>\r\n      <version>1.6.0<\/version>\r\n    <\/dependency>\r\n  <\/dependencies>\r\n<\/project>\r\n<\/pre>\n
Then I looked into Apache Mahout<\/a> examples and algorithms available for text classification problem. The most simple and accurate one is Naive Bayes classifier<\/a>. Here is a code snippet:
 
<\/div> <\/div><\/p>\n
package org.acme;\r\n\r\nimport java.io.BufferedReader;\r\nimport java.io.IOException;\r\nimport java.io.FileReader;\r\nimport java.util.List;\r\n\r\nimport org.apache.hadoop.fs.Path;\r\nimport org.apache.mahout.classifier.ClassifierResult;\r\nimport org.apache.mahout.classifier.bayes.TrainClassifier;\r\nimport org.apache.mahout.classifier.bayes.algorithm.BayesAlgorithm;\r\nimport org.apache.mahout.classifier.bayes.common.BayesParameters;\r\nimport org.apache.mahout.classifier.bayes.datastore.InMemoryBayesDatastore;\r\nimport org.apache.mahout.classifier.bayes.exceptions.InvalidDatastoreException;\r\nimport org.apache.mahout.classifier.bayes.interfaces.Algorithm;\r\nimport org.apache.mahout.classifier.bayes.interfaces.Datastore;\r\nimport org.apache.mahout.classifier.bayes.model.ClassifierContext;\r\nimport org.apache.mahout.common.nlp.NGrams;\r\n\r\npublic class Starter {\r\n public static void main( final String[] args ) {\r\n  final BayesParameters params = new BayesParameters();\r\n  params.setGramSize( 1 );\r\n  params.set( \"verbose\", \"true\" );\r\n  params.set( \"classifierType\", \"bayes\" );\r\n  params.set( \"defaultCat\", \"OTHER\" );\r\n  params.set( \"encoding\", \"UTF-8\" );\r\n  params.set( \"alpha_i\", \"1.0\" );\r\n  params.set( \"dataSource\", \"hdfs\" );\r\n  params.set( \"basePath\", \"\/tmp\/output\" );\r\n  \r\n  try {\r\n      Path input = new Path( \"\/tmp\/input\" );\r\n      TrainClassifier.trainNaiveBayes( input, \"\/tmp\/output\", params );\r\n   \r\n      Algorithm algorithm = new BayesAlgorithm();\r\n      Datastore datastore = new InMemoryBayesDatastore( params );\r\n      ClassifierContext classifier = new ClassifierContext( algorithm, datastore );\r\n      classifier.initialize();\r\n      \r\n      final BufferedReader reader = new BufferedReader( new FileReader( args[ 0 ] ) );\r\n      String entry = reader.readLine();\r\n      \r\n      while( entry != null ) {\r\n          List< String > document = new NGrams( entry, \r\n                          Integer.parseInt( params.get( \"gramSize\" ) ) )\r\n                          .generateNGramsWithoutLabel();\r\n\r\n          ClassifierResult result = classifier.classifyDocument( \r\n                           document.toArray( new String[ document.size() ] ), \r\n                           params.get( \"defaultCat\" ) );          \r\n\r\n          entry = reader.readLine();\r\n      }\r\n  } catch( final IOException ex ) {\r\n   ex.printStackTrace();\r\n  } catch( final InvalidDatastoreException ex ) {\r\n   ex.printStackTrace();\r\n  }\r\n }\r\n}\r\n<\/pre>\n
There is one important note here: system must be taught before starting classification. In order to do so, it’s necessary to provide examples (more – better) of different text classification. It should be simple files where each line starts with category separated by tab from text itself. F.e.:<\/p>\n
SUGGESTION  That's a great suggestion\r\nQUESTION  Do you sell Microsoft Office?\r\n...\r\n<\/pre>\n
More files you can provide, more precise classification you will get. All files must be put to the ‘\/tmp\/input’ folder, they will be processed by Apache Hadoop<\/a> first. :)<\/p>\n
Reference: <\/i><\/strong>Getting started with Apache Mahout<\/a> from our JCG partner<\/a> <\/strong><\/span><\/span>Andrey Redko at the Andriy Redko {devmind}<\/a>. <\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"
Recently I have got an interesting problem to solve: how to classify text from different sources using automation? Some time ago I read about a project which does this as well as many other text analysis stuff – Apache Mahout. Though it’s not a very mature one (current version is 0.4), it’s very powerful and …<\/p>\n","protected":false},"author":141,"featured_media":72,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[8],"tags":[366],"class_list":["post-916","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-enterprise-java","tag-apache-mahout"],"yoast_head":"\nApache Mahout: Getting started - Java Code Geeks<\/title>\n<meta name=\"description\" content=\"Recently I have got an interesting problem to solve: how to classify text from different sources using automation? 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